{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8d45c8ce-02bf-4c21-b0a9-ad3aa2fcf1e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import os\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from cycler import cycler\n",
    "\n",
    "thepath=os.getcwd()+r\"\\parties4dataverse.csv\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "708a4292-5feb-4a72-bce4-3f1cf4a9adae",
   "metadata": {},
   "outputs": [],
   "source": [
    "parties0=pd.read_csv(thepath,low_memory=False,index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "221ff716-a72f-4021-979c-8f8e81e6d5c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Included variables\n",
    "#  'year',\n",
    "#  'iso3n',\n",
    "#\n",
    "#  COMPARATIVE MANIFESTO PROJECT VARIABLES: The Manifesto Data Collection, Manifesto Project (MRG/CMP/MARPOR), Version 2024a. https://doi.org/10.25522/manifesto.mpds.2024a.\n",
    "#  'cmp_party_id', #CMP & PIMPO party ID\n",
    "#  'per607_2','per608_2','per601_2','per602_2',\n",
    "#\n",
    "#  PIMPO: Parties' Immigration and Integration Positions Dataset (Supplements Comparative Manifesto Project): Lehmann, Pola, and Malisa Zobel. 2018. “Positions and Saliency of Immigration in Party Manifestos: A Novel Dataset Using Crowd Coding.” European Journal of Political Research 57 (4): 1056–1083. https://doi.org/10.1111/1475-6765.12266.\n",
    "#  'totals',\n",
    "#  'totals_immi',\n",
    "#  'totals_inti',\n",
    "#  'saliency',\n",
    "#  'saliency_immi',\n",
    "#  'saliency_inti',\n",
    "#  'immi_pos',\n",
    "#  'immi_pos_saliency',\n",
    "#  'inti_pos',\n",
    "#  'inti_pos_saliency',\n",
    "#\n",
    "#  VARIABLES FROM V-PARTY DATASET: Lindberg, Staffan I., Nils Düpont, Masaaki Higashijima, Yaman Berker Kavasoglu, Kyle L. Marquardt, Michael Bernhard, Holger Döring, et al. 2022. Varieties of Party Identity and Organization (V–Party) Dataset V2. Gothenburg: Varieties of Democracy (V–Dem) Project. https://doi.org/10.23696/vpartydsv2.\n",
    "#  'v2paenname', #V-Party dataset name\n",
    "#  'v2paid', #V-Party dataset ID\n",
    "#  'v2pagovsup', \n",
    "# 'v2pasalie_3',\n",
    "# 'v2pasalie_4',\n",
    "# 'v2pasalie_5',\n",
    "# 'v2pasalie_6',\n",
    "# 'v2pasalie_7',\n",
    "# 'v2pasalie_8',\n",
    "# 'v2pasalie_9',\n",
    "# 'v2pasalie_10',\n",
    "# 'v2pasalie_12',\n",
    "# 'v2pasalie_13',\n",
    "# 'v2pasalie_15',\n",
    "#  'v2paimmig_osp', \n",
    "#  'v2paimmig_ord',\n",
    "#\n",
    "#  #WORLD BANK DATA : World Bank. 2024.World Development Indicators.Washington, DC: World Bank. https://data.worldbank.org/products/wdi.\n",
    "#  'WBgroup',\n",
    "#  'pop',\n",
    "#  'intlmigpc'\n",
    "# 'fbquantile' # Migrant share of population quartiles\n",
    "#\n",
    "#  #V-DEM DATA: V-Dem [Country-Year/Country-Date] Dataset v14. Gothenburg: Varieties of Democracy (V-Dem) Project. https://doi.org/10.23696/mcwt-fr58.\n",
    "#  'democracy' # Liberal and electoral democracies per V-DEM's 'v2x_regime'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6201dfab-32e8-45ec-87f6-323322b535bb",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make variables from CMP data\n",
    "#  'per601_2', # National Way of Life: Immigration: Negative - Only concerned with the possibility of new immigrants. For negative statements regarding immigrants already in the manifesto country,\n",
    "# 'per602_2', #National Way of Life: Immigration: Positive - statements favouring new immigrants; against restrictions and quotas; rejection of the ‘boat is full’ argument.\n",
    "# 'per607_2', # Multiculturalism: Immigrants Diversity - Statements favouring the idea that immigrants keep their cultural traits; voluntary integration; state providing opportunities to integrate.\n",
    "# 'per608_2', # Multiculturalism: Immigrants ssimilation - Calls for immigrants that are in the country to adopt the manifesto country’s culture and fully assimilate. Reinforce integration. Only concerned with immigrants already in the manifesto country.\n",
    "cmpvariabledict={}\n",
    "cmpvariabledict.update(\n",
    "    {'immiginti_percent':['per607_2',\n",
    "                          'per608_2']})\n",
    "cmpvariabledict.update({'immiginti_pro_percent':['per607_2']})\n",
    "cmpvariabledict.update({'immiginti_anti_percent':['per608_2']})\n",
    "cmpvariabledict.update({'newimmig_percent':['per601_2','per602_2']}) \n",
    "cmpvariabledict.update({'newimmig_pro_percent':['per602_2']})\n",
    "cmpvariabledict.update({'newimmig_anti_percent':['per601_2']})\n",
    "\n",
    "for k,v in cmpvariabledict.items():\n",
    "    parties0[k]=parties0[v].sum(min_count=1,axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "696870b0-e144-4280-883d-b8d80848279e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Make variables from PIMPO data\n",
    "# total = total quasi-sentences dealing with immigration\n",
    "# totals_immi = Total QS on immigration\n",
    "# totals_inti = Total QS on integration\n",
    "# immi_pos = (positive re immig - negative re immig)/(positive+neutral+negative)\n",
    "# inti_pos = (positive re integration - negative re integration)/(positive+neutral+negative) - positive values mean more liberal - pro integration cf pro assimilation\n",
    "# Immi_pos_saliency = gives the percentage of directional quasi-sentences. It is calculated by adding the supportive and the sceptical immigration quasi- sentences and dividing them by all immigration quasi-sentence (supportive, sceptical and neutral)\n",
    "# Inti_pos_saliency - gives the percentage of directional quasi-sentences. It is calculated by adding the supportive and the sceptical integration quasi- sentences and dividing them by all integration quasi-sentence (supportive, sceptical and neutral)\n",
    "for x in ['immi','inti']:\n",
    "    parties0['neutralQS_'+x]=parties0.apply(lambda row: ((100-row[x+'_pos_saliency'])/100)*row['totals_'+x],axis=1)\n",
    "    parties0['positiveQS_'+x]=parties0.apply(lambda row: (row[x+'_pos']*row['totals_'+x]+row['totals_'+x]-row['neutralQS_'+x])/2,axis=1)\n",
    "    parties0['negativeQS_'+x]=parties0.apply(lambda row: (row['totals_'+x]-row['neutralQS_'+x]-row['positiveQS_'+x]),axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "873d66af-a814-4273-87ce-74ab6f516df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Use PIMPO to fill in missing CMP\n",
    "for c,p in zip(['newimmig_pro_percent','newimmig_anti_percent','immiginti_pro_percent','immiginti_anti_percent'],\n",
    "               ['positiveQS_immi','negativeQS_immi','positiveQS_inti','negativeQS_inti']):\n",
    "    parties0.loc[pd.isna(parties0[c]),c]=parties0[p]/parties0['totals']*100\n",
    "parties0.loc[pd.isna(parties0['immiginti_percent']),'immiginti_percent']=parties0['immiginti_anti_percent']+parties0['immiginti_pro_percent']\n",
    "parties0.loc[pd.isna(parties0['immiginti_percent']),'newimmig_percent']=parties0['newimmig_anti_percent']+parties0['newimmig_pro_percent']\n",
    "parties0['immigall_negpercent_cmp']=parties0['newimmig_anti_percent']+parties0['immiginti_anti_percent']\n",
    "parties0['immigall_pospercent_cmp']=parties0['newimmig_pro_percent']+parties0['immiginti_pro_percent']\n",
    "parties0['immigall_percent_cmp']=parties0['immigall_negpercent_cmp']+parties0['immigall_pospercent_cmp']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "b8e67ec5-99c5-44ad-88b0-262af2994215",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>fbquantile</th>\n",
       "      <th colspan=\"3\" halign=\"left\">immigall_pospercent_cmp</th>\n",
       "      <th colspan=\"3\" halign=\"left\">immigall_negpercent_cmp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>median</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Highest quartile</td>\n",
       "      <td>1.378266</td>\n",
       "      <td>0.7210</td>\n",
       "      <td>384</td>\n",
       "      <td>1.312198</td>\n",
       "      <td>0.121507</td>\n",
       "      <td>384</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Lowest quartile</td>\n",
       "      <td>0.116453</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>137</td>\n",
       "      <td>0.014263</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Second quartile</td>\n",
       "      <td>0.136927</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>137</td>\n",
       "      <td>0.334891</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Third quartile</td>\n",
       "      <td>1.073849</td>\n",
       "      <td>0.2765</td>\n",
       "      <td>484</td>\n",
       "      <td>1.219207</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>484</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         fbquantile immigall_pospercent_cmp                \\\n",
       "                                       mean  median count   \n",
       "0  Highest quartile                1.378266  0.7210   384   \n",
       "1   Lowest quartile                0.116453  0.0000   137   \n",
       "2   Second quartile                0.136927  0.0000   137   \n",
       "3    Third quartile                1.073849  0.2765   484   \n",
       "\n",
       "  immigall_negpercent_cmp                  \n",
       "                     mean    median count  \n",
       "0                1.312198  0.121507   384  \n",
       "1                0.014263  0.000000   137  \n",
       "2                0.334891  0.000000   137  \n",
       "3                1.219207  0.000000   484  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Percent of platform about immigration per CMP - see FN 62 in Chapter 7\n",
    "parties0.loc[((parties0.democracy==1)&(parties0.WBgroup!='L')&(parties0.year>=2000))].groupby(['fbquantile'],observed=False)[['immigall_pospercent_cmp','immigall_negpercent_cmp']].agg(['mean','median','count']).reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b763c7b7-a0d8-457e-8352-fc3fbadd79bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the position of each party after the election \n",
    "# Question: Does this party support the government formed immediately after this election?\n",
    "# Clarification: This question refers to the initial support, by the party, of the first government formed\n",
    "# based on this election. It does not include caretaker cabinets that are in office until the first\n",
    "# cabinet forms.\n",
    "# Responses:\n",
    "# 0: Yes, as senior partner. The Head of Government belongs to this party.\n",
    "# 1: Yes, as junior partner. The Head of Government does not belong to this party, but one or\n",
    "# more cabinet ministers do.\n",
    "# 2: Yes, but the party is not officially represented in government.\n",
    "# 3: No, party is in opposition to the government.\n",
    "# 4: Not applicable. No government took office based on this election (yet)\n",
    "conditions=[\n",
    "    (parties0.v2pagovsup==0),\n",
    "    (parties0.v2pagovsup.isin([1,2])),\n",
    "    (parties0.v2pagovsup==3)\n",
    "]\n",
    "choices=['Senior','Junior','Opposition']\n",
    "parties0['exec_pos']=np.select(conditions,choices,default=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9a9669e2-6f25-4f10-bd19-0c4083ed038a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Get the position of political party going in to the election\n",
    "#Create election numbers\n",
    "vpartyelectionnumbers=parties0[['alpha3','year']].copy().drop_duplicates()\n",
    "vpartyelectionnumbers['election_sequence']=vpartyelectionnumbers.sort_values(by=['alpha3','year']).groupby(['alpha3']).cumcount()\n",
    "vpartyelectionnumbers['prev_election_sequence']=vpartyelectionnumbers['election_sequence']-1\n",
    "vpartyelectionnumbers.loc[vpartyelectionnumbers.election_sequence==0,'prev_election_sequence']=np.nan\n",
    "\n",
    "vparty=parties0.merge(vpartyelectionnumbers[['alpha3','year','election_sequence','prev_election_sequence']],how='left')\n",
    "prev=vparty[['alpha3','v2paid','election_sequence','exec_pos']].copy()\n",
    "prev.columns=['prev_'+x if x not in ['v2paid','alpha3'] else x for x in prev.columns]\n",
    "vparty1=vparty.merge(prev,how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "01a2cac8-caad-4e21-8820-a8b8cf3daa21",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "immig_salience\n",
       "1    284984\n",
       "0      4489\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Get most salient issue per VParty\n",
    "# 'v2pasalie_3': Salience of minority rights in party's appeals (0-1)\n",
    "# 'v2pasalie_4': Salience of immigration in party's appeals\n",
    "# 'v2pasalie_5': Salience of LGBTQ rights in party's appeals\n",
    "# 'v2pasalie_6': Salience of cultural superiority in party's appeals\n",
    "# 'v2pasalie_7': Salience of religion in party's appeals\n",
    "# 'v2pasalie_8': Salience of gender in party's appeals\n",
    "# 'v2pasalie_9': Salience of welfare in party's appeals\n",
    "# 'v2pasalie_10': Salience of economy in party's appeals\n",
    "# 'v2pasalie_12': Salience of environment in party's appeals\n",
    "# 'v2pasalie_13': Salience of farm issues in party's appeals\n",
    "# 'v2pasalie_15': Salience of anti-corruption in party's appeals\n",
    "listofissues=['v2pasalie_'+str(i) for i in [3,4,5,6,7,8,9,10,12,13,15]]\n",
    "# convert to arrays\n",
    "# and reverse to preserve order\n",
    "# of min index in case of a tie\n",
    "df=vparty1[listofissues].copy()\n",
    "cols = df.columns.to_numpy()[::-1]\n",
    "a = df.loc[:, ::-1].to_numpy()\n",
    "# get the top N indices\n",
    "N=3\n",
    "idx = np.argpartition(a, -N)[:, :-N-1:-1]\n",
    "# get the top names \n",
    "names = cols[idx]\n",
    "# get the top values\n",
    "values = np.take_along_axis(a, idx, axis=1)\n",
    "# assign to new columns\n",
    "df[[f'{x}{i+1}' for i in range(N) for x in ['Max', 'ValMax']]\n",
    "  ] = (np.dstack([names,  values])\n",
    "         .reshape(len(df), -1)\n",
    "       )\n",
    "vparty2=pd.concat([vparty1,df[['Max1','Max2','Max3']].copy()],axis=1)\n",
    "conditions=[\n",
    "    vparty2.Max1=='v2pasalie_4',\n",
    "    vparty2.Max2=='v2pasalie_4',\n",
    "    vparty2.Max3=='v2pasalie_4',\n",
    "    (vparty2.Max1!='v2pasalie_4')&(vparty2.Max2!='v2pasalie_4')&(vparty2.Max3!='v2pasalie_4')&(pd.notna(vparty2.Max1))\n",
    "]\n",
    "choices=[1,1,1,0]\n",
    "vparty2['immig_salience']=np.select(conditions,choices,default=None)\n",
    "vparty2['immig_salience'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "7429af48-8312-4a79-afed-dba2e2f2fac7",
   "metadata": {},
   "outputs": [],
   "source": [
    "#'v2paimmig_ord' and v2paimmig_osp\n",
    "# Question: What is the party’s position regarding immigration into the country?\n",
    "# Clarification: Immigration refers to individuals entering the country for an indefinite, long-term or\n",
    "# permanent period of time.\n",
    "# Responses:\n",
    "# 0: Strongly opposes. This party strongly opposes all or almost all forms of immigration into\n",
    "# the country.\n",
    "# 1: Opposes. This party opposes most forms of immigration into the country.\n",
    "# 2: Ambiguous/No position. This party has no clear policy with regard to immigration into the\n",
    "# country.\n",
    "# 3: Supports. This party supports most forms of immigration into the country.\n",
    "# 4: Strongly supports. This party strongly supports all or almost all forms of immigration into\n",
    "# the country.\n",
    "conditions=[\n",
    "        vparty2['v2paimmig_ord']<=1,\n",
    "        (vparty2['v2paimmig_ord']==2)&(vparty2['immig_salience']==1)&(vparty2['v2paimmig_osp']<2), #Force parties to have a position if immigration is a top 3 most salient issue for them\n",
    "        (vparty2['v2paimmig_ord']==2)&(vparty2['immig_salience']==1)&(vparty2['v2paimmig_osp']>=2), #Force parties to have a position if immigration is a top 3 most salient issue for them\n",
    "        (vparty2['v2paimmig_ord']==2)&(vparty2['immig_salience']==0),\n",
    "        vparty2['v2paimmig_ord']>2\n",
    "             ]\n",
    "choices=[1,1,0,0,0]\n",
    "vparty2['immig_anti']=pd.to_numeric(np.select(conditions,choices,default=None))\n",
    "choices=[0,0,1,0,1]\n",
    "vparty2['immig_pro']=pd.to_numeric(np.select(conditions,choices,default=None))\n",
    "choices=[0,0,0,1,0]\n",
    "vparty2['immig_none']=pd.to_numeric(np.select(conditions,choices,default=None))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "c011707b-8da4-469a-a849-cda1a9f46122",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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WrFnNZZddZvuExzPOtoEQZ6K1atXK79ecIJlu4xtE0yhhRe+9Va1aNaBBBLq/GkBqcokCdBUqVEj39wEAAAAAgLc8efLYSyKjnBMIkX379plt27Yl20BRzQmVXaYgmjcFyhQw81f+qdueOXMm1SCahhgom+23335L53cBAAAAAAD8IYgGRGCoQHLDBVR+uXTpUhsw86XAmkYLO/3R/HHuX6dOHVuzTl80AAAAAADCgyAaEMIgmqZrKtssOQqW6XZOdpnKL//5559kM9EkpZLOVatWee6vho9kogEAAAAAEB4E0YAQUenllVdeafuTpZSJdvjwYbNx48YkQwH8lYDmzp3bFCtWLMUJnc7X9LjVqlWzWWknT54MwXcDAAAAAAC8EUQDQjyZMyVOqafTF01ZZiVLlrTTOP1RhllqQbRSpUrZfmvKRFMAbfny5en6PgAAAAAAwLkIogEhoJJKlWam1A9NLrnkEjv21ynRVBDMXymn74TO5IYLeA8lUBZcpkyZ6IsGAAAAADGmbt265tFHH4348xYtWtT069fPRLs5c+bYYXoHDx50dR0E0YAQ+PPPP83p06dTzURzep0p+PXvv//a7DV/QwUcCpAdOXLEbNiw4Zyv6fl0fyeIljVrVlOhQgX6ogEAAABAjBk/frx57bXXIv68On988MEHTbQHFGvXrm127NiRbBVXpBBEA0JA5ZnqhaYgVmoU9JoxY4bJkiWLOXbsmO1llhwnwOavpFOZb8ePH0+SyabHiqdMtLvvvts8//zzbi8DAAAAAMIqT548JkeOHBF/3nz58tkBedEuc+bMpkCBAjYbzU0E0YAQUEZYmTJlbDZYah555BHz6aefmpEjR5qvvvrK1KlTJ9nbXnzxxaZIkSJ+g2jeQwW8g2jKilNwLdbpe5g4caIZO3as20sBAAAAEIPOnj1rTp065epFawg2+0ollq+//rpp06aNufDCC+054aRJk8yePXtM48aN7XWVKlVKkkAxYsQIkytXLjN58mRTunRpGxi766677HmVzj31mBpe9/DDD9uqpuTKOZWsce2119qkj3LlypmZM2fawNWECRMC+j569Ohh+3br+YsXL25eeOEF899//3m+/vLLL9sKLp0T67mVWdayZUtbgSVt27Y1P/74o3n//fft8+qyefPmqCnnzOTqswNxlImWWj80h17Y7r333oAfO7nhArrOdyiBhgvoBVFTOmvVqmVi2bx58+yL7bp168y2bdtM4cKF3V4SAAAAgBiicyMFhdykZAv1rg7We++9Z3r37m2DUPr4vvvusyWN7du3N3369LHBKgXZVqxY4cnOUsDsgw8+MKNHj7ZBqaZNm5omTZrYc9CpU6eajRs3mmbNmplrrrnGtGjRwu/P68477zSXX365WbBggX2MJ554Iqh158iRwwb0Lr30Ujv0rmPHjva6p59+2nMbtStSUE4BvwMHDpjmzZubN9980/Tq1csGz9auXWurvF599VVPtpwCadGATDQgnfRCs2zZsoD6oaWF00PNdwfD31ACvdAozTUeSjpnz55tU5r1hqCPAQAAACBRNGzY0HTq1MkmTrz44ovm8OHDNmlCLW+U6aUg2qpVq8yuXbs891ESwsCBA22Cx/XXX28z0ZSc8Mknn9issttuu83ccMMN5ocffvD7nN9//70NcI0aNcoOrlNGmgJbwXj++edtsE9ZZrfffrt58sknz6ku0uA8Bdp0/nrdddfZAOGsWbPs15QkonNaZbKpfFMXtU6KFgTRgHRSppQi/oFmogVLgbJDhw7ZXQPvwJ2y33yDaHqxUTAvHoYL6EW0QYMG9vtxXlABAAAAIBGoXNORP39++2/FihXPuW737t2e6xR4KlGiRJLbKJil8k/v67zv423NmjW2AkiBK0eNGjWCWveYMWNsppseQ8+roNrWrVuT3EZr8u7/VrBgwWTXFG0IogEh6Icm4cpEcwJl3iWdenHzHSoQT8MFlNKr7/fGG2+0FwXRAu0lAAAAAACx7vzzz/d87JRr+rtOWV3+7uPcxt913vcJpV9++cW0bt3aZtGpVFOJHz179jQnT55McrtIrinU6IkGpJNeGFQzrtLDcFD9t3YDFFRSrXhyQwUcSvFVCq/q192Y7hIKaiSpoFm9evXsrsQ777xj6+LVIBMAAAAAAqEyQPUkc3sNsULnW+pHrRJRJ9MtmCqnn3/+2Q5BUODMsWXLlqDXoQor7+EH0YQgGhCCIFq4stB8+6I5FERTmq4aRPrLRFMASrdPafJnNFPmmSa5KM03b968thGnriOIBgAAACBQynBKS1P/RHXzzTfb88z777/fvP322zYxQ+WY3plvKVH/NpVuarCBkjumTJlivvnmGxMsnQdqsIGGCagkNFwJK2lBOSeQDgpWqZwzXP3QfCd0OiWN/oYKOMqWLWtr4WO5L5oGCaiMU/SiefXVVzNcAAAAAADCnDWnqZlHjx61QbAOHTp4ssqyZMmS6v3vuOMO89hjj5lu3brZRBNlpmm6aLA0jEBr0TAEVWb59lRzU4azCdZoSBMtNO1Bjdpz5szp9nIQ47Zv324KFSpko+saBRwuGkfcqFEjO1xApaP6HdaEFu8xwd404eSyyy6zOwCxZseOHXYc8pdffmlatmxpr3vppZdM//79zZ49e8x55xH7BwAAAODfiRMnzKZNm0yxYsUCCvwgZfPnz7dTOtevX59kaEG8/W4EGivibBQIwVCBSGSiORlomgZ67NixZDPRnJLOWM1Ec8Yta/SyQ1lp+/fvN0uXLnVxZQAAAAAQ35Qg8v3339tSypkzZ5oHH3zQTtuM9QBaqFAcDKSzH1ru3Lltdlg4qamjsrPef/992yPM6ZOWHKXe9uvXz6bSavKJXviuuOIKE61mzJhhL85QgQoVKngaWUrNmjVN1qxZzTPPPJNkrHNymjRpYl/oAQAAAACBUx+0Hj162BJKnXvedNNN5t1337Vf6927t70kVw01bdo0E+8IogHpzES78sorA2qymF4PPPCAGTt2rC1pVJmjgnfJ0VRLBdn0IqZpKBoprKBatHr44YfN3r17PQHC7t27J/n6BRdcYLp06WIbU6Y23WX37t1m3rx55tdffw3rmgEAAAAg3rRp08Ze/OncubNp3ry5368p6SER0BMNSAdNH7n99ttN3759TbRq0aKF+fvvv81PP/1korkHmvq3aa3pNWrUKDtNRo9boECBkKwRAAAAQGygJxqSQ080wOU0VzVX1NSRaKbeaSo7PXPmjIlGc+bMsf/WqVMnJI/XsGFDO3xg8uTJIXk8AAAAALEnwfKFEKHfCYJoQBotW7bM/qtyzmgPomkQwdq1a020DhIoW7ZsyLLGVBKqfmiTJk0KyeMBAAAAiB0ZM2a0/6qlDeDt+PHj9l/1DU8reqIB6eiHpj8+BYCimTOAQJM9y5QpY6IxE+3mm28O6WPecccd5oUXXrDBw+zZs4f0sQEAAABEr0yZMpls2bLZXtI6X1OVChLb2bNnbQBN/bNz5crlCbSmBUE0II2WLl1qypcvbzJnzmyimQYQFC9e3AbRWrdubaLJ9u3bzbp165Kd8JKeINpTTz1lRzI3btw4pI8NAAAAIHpp6FvBggVt76vUhpIhseTKlSvdFVAE0YB0ZKJFez8072w0BdHivR+ao1SpUjbrTiWdBNEAAACAxKJEBw2Bo6QTDmUlpicDzUEQDUiDU6dOmeXLl5tWrVqZWKC+aMr20nCBaEpnVj+0ChUqmHz58oX8sZWNNnz4cHP69OmQvFgCAAAAiB0672E6J0Ites6mgRiiJv0ajxsrmWgKojnTRKOJgmg33HBDWB5bQTT1QVi4cGFYHh8AAAAAkFgIogFp7IcWC5M5/Q0XiBZbt241GzduDFsQ7eqrr7aTOidOnBiWxwcAAAAAJBaCaEAa+6EVKVLENu2PBRdffLFdr9tBNE1FOXDggNm/f7+ZNm2abfp5/fXXh+W5VMJ522232b5oAAAAAACkF0E0II1BtFjJQvMu6XQ7iPbee++ZPHny2KBe586dbTmsPg4XDRVYtWqVnQAKAAAAAEB6EEQD4nwyp3cQbcmSJXa4gFuUfVa7dm0zfvx4e/nqq6/C+nw333yzueCCC8y3334b1ucBAAAAAMQ/gmhAkHbu3Gl2794dk0G0w4cP2z5kbk00/fXXX23D/yZNmthLiRIlwvqc2bNnNzfddBMlnQAAAACAdCOIBqQhC01iLYjm9nCBZcuWmaNHj5prr702os+roN28efPMvn37Ivq8AAAAAID4QhANSEMQLWfOnKZo0aImluTLl88ULlzYtSCaAlkqraxWrVpEn1fDBU6fPm2mTp0a0ecFAAAAAMQXgmhAGocKaLJkrHFzuICCaNWrV7eBtEi69NJLTY0aNSjpBAAAAACkC0E0IEhLly6NuVJO3+ECZ8+ejejz6vkURLvmmmuMG1TSOX36dPPvv/+68vwAAAAAgNhHEA0IwrFjx8yaNWtiOoh28OBBs2nTpog+7+bNm82OHTsi3g/NO4imfmxz5sxx5fkBAAAAALHP9SDaRx99ZHtLZcmSxdSsWdMsXLgwxdsrAPDQQw+ZggUL2rKwUqVK0esIEfPnn3/arCqVc8Yit4YLKAtNateubdxQoUIF+zpDSScAAAAAICaDaGPGjDGPP/64eemll2yJmQIT9evXN7t37/Z7+5MnT5qbb77ZZrWMGzfOZgQNGTLEXHbZZRFfOxK3H1rGjBlN+fLlTSzKnz+//XtxI4imn1mePHmMG9S/TtloCqJFupQVAAAAABAfXA2i9e3b13Ts2NG0a9fOlCtXzgwaNMhky5bNDBs2zO/tdf3+/fvNhAkTbG8lZZbUqVMnZrOCEJv90MqWLWszJ2OVG8MFFERzq5TT0bhxY/PXX3+Z33//3dV1AAAAAABiUya3nlhZZTqRf/bZZz3XnXfeeeamm24yv/zyi9/7KIukVq1atpxz4sSJJl++fKZVq1amR48eNjvIHzUS924mfvjw4TB8N4ll2rRp9ud/0UUXmddff92cf/75nq9t377dvPnmm+a///7zXKf/myeeeMIUL17cRCMFZl955RX7e5IjRw77PflOkOzXr59ZvXq1mTJliqlbt66JZQqi9enTx3Tu3Dlkj5kzZ077c8ucOfM5X9u3b59ZuXJlkr91N1x33XX2d/aRRx5JNpNQgflnnnkm4msDAAAAAEQ/14Joe/fuNadPn7blZd70uYIV/mzcuNHMnj3btG7d2vZBW79+venatasN2Kgk1J833njDBkgQOvqZKptHjdrvvvtuU61aNc/Xxo8fbwYMGODpvSXLli0zl156qenZs6eJRt9++6354IMPbEajMs0aNmxobrjhBs/X9X2q7FhBQPXiu+eee0wsu/POO+2kylBlo506dcqWud5yyy02CO7r559/tv+6NZnToWCvAmRff/213+/9+PHj5uOPP7Zln8qMBQAAAAAgKoJoaXHmzBlzySWXmMGDB9vsJmXUKPNJWTXJBdGU/aIAiHcmWuHChSO46vijyY733nuvLb9VfzrvIJq+VqJECfPbb795rrv66qvNunXrTLRSkE8BskWLFpkLL7zQfu4dRHOGCYwdOzZJcDBWVapUyRPYCgX9bJQVqpJNf0G0+fPn2yCqsrzcpiBacplmJ06csK8vX331VbKvJwAAAACAxOVaT7S8efPaQNiuXbuSXK/PCxQo4Pc+ygLSNE7v0k31p9q5c6ctD/VHZXkqNfO+IO30c1bgUgFMlT4qaOZNQbVixYolua5kyZJRH0RTYClTpkx2iqOy0bzpc/3OkZ2UfNN+ZZk5EziT64em20Uz9bm7/fbb7dASAAAAAACiJoim3kkKxMyaNStJppk+V98zf3SirhJO3c6xdu1aG1zz14sJobdt2zabeaRAmTKLFDTzpqCabxBNgc9YCKKJ/tXnvkG0MmXKxPQwgXDT3+avv/6apBeek92lrES3hwoESuXJyjxMrqQcAAAAAJC4XJ3OqTLLIUOGmJEjR5pVq1aZLl26mGPHjtlpndKmTZskzcj1dTWBV2NwBc/U5L1379520AAiwwmaKYCmYJlvJpq/IJoy0fbs2WMOHjxooo0yH3fv3u0JoqkvmoIo6vPlHURzvg7/FCTT365vFp9KZJW9GCtBtPr169uSXpV0AgAAAAAQNUG0Fi1amHfeece8+OKLpnLlyrY5uRqeO8MGtm7danbs2OG5vXqZfffddzazRUGNhx9+2AbUmKYXOQqSqSxP/xcKpHkH0Q4cOGAOHTp0Tu8rBdEkGrPRnKwz70w0Tel01qqsx+XLl9vgGpKnrFKVTqv/mW8pp4JSFStWNLEga9astqSTIBoAAAAAIOoGC3Tr1s1e/JkzZ84516nUU2VjcC8TrVChQrZ8Vhln+lzlnQqsOVlq/jLRRIGp6tWrm2gLomXLls0OFvAOpul69dvT93TkyBGCaKlQAK1GjRo2aKbAtkOf629W/eZihUo6v/zyS7NmzRpTunRpt5cDAAAAAIgSrmaiIfYoqORkmilYpp5XznAIJyvNN4imYQ6aehitmWgaJuAMq7j44ovNZZdd5ilLdP4liJY6lWwqaKagqpPFpymgsVLK6WjQoAElnQAAAACAcxBEQ5qDaM6/TgaagmjZs2e3k1d9ReuETu+hAg4FzJwyTwXR8uXLl+zEWPwfBcs0KXfjxo32c/U5VIlvrAXRVNJ52223EUQDAAAAACRBEA1BUaDMN4jmZKA5X1Npp78gmoZBRBNNkly5cuU5QTR97mSgOUE2f98TklLZpn5OykYT/asMv5o1a5pYo5JO/d9H2+8sAAAAAMA9BNGQLJXl/fXXX2b79u32czXc//vvvz3lmhdddJHJnTu3HQixZcsWs3r16nNKOX0z0XQ7XbZt2+Yp+/OlKZ66jSaxhvr70ffirGHu3Ll2cqS/TDR93xoo8Pvvv1PKGSD9Lqg0dsaMGfbnO3PmTHPVVVfZ7MRYo5JO9cqL5Wy048ePu70EAAAAAIgrBNGQLDVX1xRODRIYN26cnZYq3tM31Xj97bffttfNmjXLlCpVyu9jlS9f3gbHdDtdLr/8cjNkyJBzbqdAXZEiRextVEK5e/fukH0/+h70vThruOmmm2ymlG8QTYEf0fUqVdXkSQSmTp065osvvrA/X/289XksUgBNJZ36HmLRkiVLPAFuAAAAAEBoxM7IPEScMrEUyDp16pT5888/beaZbxBt7NixNgPNkVzpngISP/74ow2SiSay6kTf14YNG8zhw4fNM888Y958802zYsUKO5QgFBYsWGCDgp988onnOj22gg3eFAhcuHChDfqdf/75MdfTy029evUyjRs39kxsvfrqq02sUkmnLuvXrzdXXHGFiSUjR460WZb6t3Llym4vBwAAAADiQoazydXUxSkFaBQMOnTokJ0aieS1atXKljXqZLxs2bK251WXLl3sRE4Fl9KjRYsWNsvshx9+SHL9N998Y5o2bWqz3lQa+tFHH5lOnTqZUJXoZcmSxUyYMCEkj4f4pnJIDZV4/vnnzbPPPmtihYLeyrh0Xuv0N+xMnwUAAAAApD1WRDknkqW+Vk5ppcoadVEmV3oDaE4Z6Jo1a865XtfpF1ZBgOLFi4e0sbuy6dSzCwi0pLNRo0Yx1xdNgeldu3aZd999105LnT17tttLAgAAAIC4QBANyVI2mIJouiigpiCadylneoNoO3bssNFebwqa6WsqBVRZpb9AW1ocOHDADhVQbzYgUCrn1HAJlRnHUi9DlZ927tzZDvT4/PPP3V4SAAAAAMQFgmjw67///rOTOJ1MNE3TVG+oUAXRnAEEvplmCpo5X9O/ocpEU281IRMNwWjYsKHJmjVrzGSjqdT666+/tqXYCkS3bt3ajB8/3vzzzz9uLw0AAAAAYh5BNPilPkpnzpyxUzQVSFOfJU36C2UmmvhmmjmZaE4QbePGjTagF4pSzkyZMnkeGwhE9uzZY6qkc9q0aTa785577rGfK4h25MgR8+2337q9NAAAAACIeQTR4JfKN8XJRBMFs9TsPxTU90yTP72DaPv37zd79+71BLr07+nTp82mTZtCEkRTUC5z5szpfiwkXkmnJskqoBvtvvjiC1OlShVTpkwZ+7nKOmvUqEFJJwAAAACEAEE0pBhEczLRHKHKRPM3XMD52LucU0JR0slQAaSVMtFioaRTGWjKOFMppzdloylDTUFqAAAAAEDaEURDskMF8uXLZycUqqQtb968YQ+iOcEyNUOXSy+91D5/eocLnD17liAa0ky//+qNNm7cOBPNJkyYYP7991/TokWLJNfrc5VmR3sQEAAAAACiHUE0JJuJ5puBpp5il112WUiDaAqc6QRfFCwrVKiQDVqIM6EzvZlou3btMvv27SOIhnSVdC5atCgkpcXhLOW8/vrrTeHChZNcnz9/fnPTTTdR0gkAAAAA6ZQpvQ+A6DZ16lR78ly5cmXz1FNPpTmIpo8ViFIgLZRBNE0NbN68ubngggvML7/8ck7jfwXRJk+ebEvSRM///PPPe7LV/NFU0Z49e5qTJ0/az9VnTQiiIT0lnVmyZDH33nuvJxtTQd6HH37Y9hyLNJVmPv30056pm8q2nDlzpunfv7/f2+vvp02bNuf8Xac2XGTgwIHm1VdfNRkzZgzp+gEAAAAgFpGJFucGDBhgM1ReeOEFe6IdKJ1sqx+ao23btubRRx8N6dpq165tbrvtNhucU+BLJ/ft27dPcpv77rvPBtL0dV3GjBmTalnaxIkTzaeffuq5j4JpCiIUL148pOtH4rjwwgvNSy+9ZAdTOL9X33//vXn99dddWc/IkSPNqFGjPGvZsWOHLTlt2bKl39vfeeedtq+bXgsC1bdvX9O7d28bnAMAAAAAGJPhbDCRlTig5tsXXXSROXTokJ0QGe+uuuoqs337drN7926zZ88eT2+zlOhXQifcb731lnnkkUdMNLnmmmvshNDPPvss2ds89NBD5scff7R90IBw+eCDD8yTTz5py4Vz584d0eeuVq2aDXKPHz8+4Pvcc889Zvny5faiLLqUKPCs0m1lcd511130UwMAAAAQ1wKNFZGJFueUpVK9enX7sYJpgVDATQ3KAy37iqRy5cqZVatWpXgbBQko3US4Kbh06tQp29A/ktQ7cPHixZ4S50Dp9itWrDDLli1L9bZTpkyxAbQOHTrYzE4F4AEAAAAg0RFEi2P//fefDYg5PZsCDaKplFOiMYhWtmxZG0RzhhH4YhInIkXTY9XIXyXGkaQeh9ohUZ+2YNSvX99cfPHFAQ0YGDZsmA2+v/HGGzZrLaXMTwAAAABIFATR4tjOnTttUEklneedd55tFB7rQTRloqmZurNGX+oNdeDAAVOxYsWIrw2Jp0WLFrZnmDO8Itz096wgWLNmzeygg2Ccf/75dr1ffvllskFo529o2rRptj+hyr/VT+2TTz4JqqciAAAAAMQjgmhxXsop6p1UoECBoDLR1Eg90n2eAg2iSXIlnU4fNDLREAkKZim4FExvsvRYsGCB2bhxY9ClnA7dT8H0uXPnJnsbDeVQwM0ZUvDAAw/YMtCFCxemed0AAAAAEA8IoiVAEE1lZ4UKFQo4E23r1q028JZa83E3FC5c2Ab4Vq5cmWw/tGzZstnhA0C4XXLJJaZevXoRK+lUWaX+nuvUqZOm+9eqVcv+bSRX0qmAoEo5mzZtanLlymWvu+mmm+zrwdChQ9O1dgAAAACIdQTR4jyIpowS9UHSpL1gMtGisZRTFNhTX7TkgmjKRCtfvrwtXwUiQSWSc+bMsVM6w93jUME6TdnMmDFjmv9+WrVqZcaNG2eHh/j69ddf7eAClXI69LfUrl07M3r0aHP06NF0fQ8AAAAAEMuINMR5EE1ZKzpxVhAtmJ5o0RpE8x4u4A9DBRBpytpSoEmBqXD6/vvvbe+1tJZyOnT/gwcPmqlTp57zNWWh6W//hhtuSHK9gmjHjh0zX331VbqeGwAAAABiGUG0OKbMMwXRROWc8ZCJ5vRFUyaab6Pz06dP295NBNEQSXny5DE333xz2Es6VYKpAHLlypXT9Th6jCpVqpxT0qkgmbLN2rZte04mp14P9D1qwAAAAAAAJKpMbi8A4c1EUwaa6F9lnyxevNhO9StZsqQt9Vy3bp0tE3No8qVuF+1BtMOHD9vm6Joe6FCQUOtnMifcKOlUtta8efPCMpBDf6MTJkwwzz33XEh6FSobrWfPnp7XA5k+fbot11QQzR8NGND3qSxQBeLi3fHjx82mTZtSvZ1+fiVKlAj68RW03Lx5c5rWpnLeUqVKBV22fvLkSXPixAmTM2fOND0vAAAAkOgynPVN54lzCr5cdNFF5tChQ3F/IqGMrBtvvNG8//77tteRmoo7Hn/8cXPttdfaUjR/Fi1aZKpWrWqikQYfJBfk00mlgof58+eP+LqQuPR6UrBgQRvEDRf9bq9fvz4kQzMUcC5atKg5depUkuuVbTZjxgy/91EPNQXjFSzs06ePiXd33HGH+fbbbwO6rQKQ9evXD+rxGzVq5LekNlADBgwwXbp0Ceo+HTt2ND/99JPN5KVvJAAAABB8rIggWpyXmfXo0cNe9N+8bNkye5L/9NNP28l711xzjXnjjTfsCaA3TbdUNlc0Tud0KBtG/4f+vmdlaACRpqylcA4XCPXvtrJQ9+3bl+S6MmXKeKZy+vPYY4+ZL774wvZXVCZrPP9fKrvs1VdftdNJU6JAloKLkydPDvjxNbxBP+vevXuf038uEK+99ppdo8rXA32d3rNnj51urGCo1qogHgAAAIDgYkWUc8YpBcsOHDjg6YmmE60rr7zSUw65cOFCeyKsk6qrr77axJpEKCdDbFGGWCiyxCJFJd26BEMlnf369bNBmCZNmph4NWTIEPvGqaBh9uzZU7xt9+7dTYcOHWxQK9D//48++siWouvxnXLaYDz55JOmXr16dipsoEE4fU96H9Drf//+/QmiAQAAAGlAPUec2rFjh/3XCaJ5U9aEyrl0cXqmAUAgJeI1atQwQ4cONfFKfcM0QKFNmzapBtCkZcuWdsdq0KBBAT3+kSNHzIgRI8yDDz6YpgCa1K1b124kqKQzECrbHThwoGnVqpXNRFb28dq1a9P03AAAAEAiI4gWp9QXLLkgmiZ17t6922ZOEEQDEGw2moIwgU77jTUa4KDXx06dOgV0e5W/q0+cAm9q2p+akSNH2qEFwfYz86aMsq5du5pvvvkmoP8HfU/KPFbWnIZDKAtO2XAAAAAAgkMQLU45J1bJZaLJ8uXLCaIBCIoyr5RBpWyqeKSMreuvv96UL18+4PsoIKb+cmPHjk3xdmfOnLGllCqF1WZGetx33332/0Flmqn58MMP7SCZypUr2/towID+/5QVBwAAACBwBNHiOBNNpUj+GuI5gbPTp0+n+0QOQGLRa0rz5s3NsGHDbFAonqxevdr2GevcuXNQ91NvuVtuuSXV8spZs2bZoQLdunVL50qNLSFVIG3w4MHmv//+S/Z2Gigzd+5cm4XmHfQ7evSo+fTTT9O9DgAAACCREESL4yCastD8TW7zzj4jEw1AWko6N27caH788UcTTz7++GNb6ti0adOg76vyygULFpjFixenmBGmycfKdAsFPaf6X6pcM6Xn1Ou89yAIDZS58847bVZcgg3oBgAAANKFIFqcB9GSy2BwGmYTRAMQrGuuucaULl06rgYMaKKxShzbt29vLrjggqDvf9ttt5nLL7882Ww0BR011VQZYf42N9JCAbnrrrsu2f5m+/fvN59//rnNrDv//POTfE3rWLVqlZk9e3ZI1gIAAAAkAoJoCRhE0wmcEzwjiAYgWHoNUbDp66+/NgcOHDDxYMyYMebgwYMBDxTwlTFjRnvfL774wgavfCm4pg2M1q1bm1BSNpoyAlesWHHO1zTsQGX7mgTqq06dOnbaqrLRAAAAAASGIFqcSimI5gTPMmfObEuXACBYbdq0sQEaBY3iwaBBg0z9+vVN8eLF0/wYHTp0sD8T36ELmsapgJbKYDXNM5RUepo/f/5zMuC0Dl2nQRCXXHKJ30CoerNNmjTJbNmyJaRrAgAAAOJVJrcXgNBTjxtN50wtiJZczzQASE2BAgVsCaOCQw899FC6HmvlypVm+PDh5u233w7qNUnTMD/77DOTXqdOnbL9zL755pt0PY6CVXfffbd588037YACx969e82hQ4ds1lioaTNE0zb79u1rtm3b5rlekzc3b95svvrqq2Tve++995oePXrYiaRaMwAAAICUkYkWh3TydOzYsRRLNVWK9eyzz0Z0XQDiizKrfv/9d7NkyZJ0Pc7rr79u3nnnHTtFMpjA1xNPPGHWr19v0itTpkw2i0xBwfTq2bOnufbaa5Ncp4xffY/pyXJLiTLKGjVqlOS6HDlymKefftpUq1Yt2fupN6beC4YMGWJ7wgEAAABIWYazCTaa6/Dhw7YvjbICcubMaeLR6tWrTdmyZe0JqZpOA0A4KJClZvqa/Jhcc/vUKEtLAf+TJ0+aVq1a2Ub4gfj222/NHXfcYadhXnXVVWl6bhgbhCxVqpTNKGzXrp3bywEAAACiOlZEJlqc9kOTlMo5ASAUGVwKvCjwldZMplGjRtkSdGVNjRs3zuzbty+g+w0ePNhUrVqVAFo6XXHFFebWW281H374of1/AAAAAJA8gmhxHEQrWLCg20sBEOdUDqjdmvHjxwd9XwVtVEqo5vhPPvmk/XzkyJGp3k+9v6ZOnep36iSC1717d1uW+8svv7i9FAAAACCqEUSL0yBarly5Qj4FDgB8lShRwtStW9cMHTo06PvOmzfPlp8rGJYvXz4bTFOGWWoZUSo91OvbPffck46Vw3HLLbeYkiVL2mw0AAAAAMkjiBanQTRKOQFEcsCAplFu2LAhqPspYOYE4aRTp05mzZo15qeffkqxD5uCaOqfpub5SL/zzjvPTlhVOe2OHTvcXg4AAAAQtQiixaHt27cTRAMQMc2aNbNNOIcNGxbwffbv32+++uor07FjRxvEEQXT1KNLwbXkTJ8+3fz111+UcoZY27ZtzQUXXGA+/vhjt5cCAAAARC2CaHGaiaZpdwAQCVmzZjWtW7c2I0aMsJligfjss8/M6dOnbfDGkSFDBhscS2nAgII8GiagoQIIHQVB27RpY3++mpQKAAAA4FwE0eIQ5ZwA3Cjp1GuPMsVSo55nyjZr3LixyZ8/f5KvKah25swZ8+mnnyY7UEBlnwg9lXTu3LnTfP31124vBQAAAIhKBNHijE5OCaIBiDRlh1WuXNn2K0vNr7/+alasWOG3JNMZMKCMKN8BA3psZb0xUCA8ypcvb+rVq2f69+/v9lIAAACAqJTJ7QUg/TZt2mR27dplPz5y5IgtxSGIBsCNbLTHHnvMzJo1y2TPnj3Z27377rumaNGi5qabbvL7dQXXbrzxRjNy5EhTpkwZe50CagwUCL9u3brZIOaSJUtsYBQAAADA/8lw1nerP84dPnzY9n45dOiQyZkzp4kH3bt3PydzYOnSpaZSpUqurQlA4jlw4IApXLiwOXbsWKq3ffPNN02PHj38fk3lnMqKWr169TlfW7x4McGdMFJPO01MVRAzmEERAAAAQCLEigiixQGVb2rSnUMZIMWKFXN1TQAS044dO5IdCuDQNM7SpUubjBkzphiQ06RhbxdeeKHNYEN4KcD58ssv2ymoefPmdXs5AAAAQNgRREugIBoAAKGyd+9eU6hQIfPKK68kmy0IAAAAJGKsiMECAADAQ9ln6j03YMAAW94JAAAA4H8IogEAgHMGDGzdutVMnjzZ7aUAAAAAUYMgGgAASELDG2rXrm0+/PBDt5cCAAAARA2CaAAAwG822uzZs83KlSvdXgoAAAAQFQiiAQCAczRr1swUKFDA9O/f3+2lAAAAAFGBIBoAADhH5syZTadOncyoUaPslCIAAAAg0RFEAwAAfimI9u+//5oRI0a4vRQAAADAdQTRAACAXwULFjR33XWX+eijj8yZM2fcXg4AAADgKoJoAAAgWd27dzfr1q0zM2bMcHspAAAAgKsIogEAgGTVqlXLVKlShQEDAAAASHgE0QAAQLIyZMhgs9GmTp1qNmzY4PZyAAAAANcQRAMAAClq2bKlyZMnjxkwYIDbSwEAAABcQxANAACkKGvWrKZDhw5m2LBh5tixY24vBwAAAHAFQTQAAJCqLl26mMOHD5vPP//c7aUAAAAAriCIBgAAUlWkSBFzxx13mA8//NCcPXvW7eUAAAAAEUcQDQAABKRbt27mzz//NHPnznV7KQAAAEDEEUQDAAABqVevnilbtqzNRgMAAAASDUE0AAAQkAwZMthstAkTJpht27a5vRwAAAAgogiiAQCAgLVp08Zkz57dDBo0yO2lAAAAABFFEA0AAATswgsvNO3atTODBw82J06ccHs5AAAAQMQQRAMAAEHp2rWr2bt3rxk7dqzbSwEAAAAihiAaAAAISqlSpcwtt9xiPvroI7eXAgAAAEQMQTQAABC0hx56yCxcuNAsWrTI7aUAAAAAEUEQDQAABK1Ro0amSJEiZKMBAAAgYRBEAwAAQcuYMaPp3LmzGT16tNm3b5/bywEAAADCjiAaAABIkwceeMCcOXPGDB8+3O2lAAAAAGFHEA0AAKRJvnz5TIsWLczAgQNtMA0AAACIZwTRAABAugYMbNy40UyfPt3tpQAAAABhRRANAACkWY0aNUzVqlUZMAAAAIC4RxANAACkWYYMGWw22rRp02xGGgAAABCvCKIBAIB0UV+0XLly2d5oAAAAQLwiiAYAANIlW7Zspn379mbYsGHmn3/+cXs5AAAAQFgQRAMAAOnWpUsXc+DAATN69Gi3lwIAAACEBUE0AACQbiVKlDANGjSwAwbOnj3r9nIAAACAkCOIBgAAQkIDBhYvXmwWLlzo9lIAAACAkCOIBgAAQkKZaMWKFbPZaAAAAEC8IYgGAABCImPGjLY32pgxY8yePXvcXg4AAAAQUgTRAABAyGhK53nnnWc++eQTt5cCAAAAhBRBNAAAEDIXX3yxadmypRk0aJA5ffq028sBAAAAQoYgGgAACPmAgS1btpgpU6a4vRQAAAAgZAiiAQCAkKpWrZqpUaMGAwYAAAAQVwiiAQCAsGSjzZgxw6xbt87tpQAAAAAhkeHs2bNnTQI5fPiwueiii8yhQ4dMzpw53V4OAABx6cSJE6Zw4cLmvvvuM3379k3xtr/++qvZsWNHkuvy5Mlj6tSpE+ZVAgAAACbgWFGmiK4KAAAkhCxZspgHHnjAfPzxx+b111832bJl83u7DRs2mNq1axt/e3oKrtWsWTMCqwUAAABSRzknAAAIi06dOtndvNGjRyd7G03xzJUrl9m+fbvZu3evvezZs8cUKVLEDBw4MKLrBQAAAFJCEA0AAIRFsWLFzK233moHDPjLNPvnn3/MsGHDTPv27c2ll15qLr74YnvJmzev6dy5sw2+7du3z5W1AwAAAL4IogEAgLDp2rWrWbJkifntt9/O+dqYMWPM/v37TZcuXc75mgJrZ86cMSNGjIjQSgEAAICUEUQDAABh06BBA1O0aFG/pZkDBgywXy9RosQ5X7vkkkvM3Xffbe+nYBoAAADgNoJoAAAgbDJmzGh7o/mWZiozTRdlqiVHGWoaPDBz5swIrRYAAABIHkE0AAAQVv5KM5WFpuEBDRs2TPZ+11xzjalYsaK9LQAAAOA2gmgAACCsnNJMTeJUME0ZacpM0/AAZaolJ0OGDDZT7dtvvzXbtm2L6JoBAACAqAyiaWqX+qVkyZLF1KxZ0yxcuDDZ22oXWwfV3hfdDwAARC+VZq5fv96WZg4fPtwG0x544IFU79e6dWuTLVs2M3jw4IisEwAAAIjaIJomcz3++OPmpZdestO7rrzySlO/fn2ze/fuZO+TM2dOs2PHDs9ly5YtEV0zAAAITu3atU2lSpVM//797bCA5s2bm3z58qV6vxw5cpg2bdqYoUOHmpMnT0ZkrQAAAEBUBtH69u1rOnbsaNq1a2fKlStnSz204zxs2LBk76PsswIFCngu+fPnj+iaAQBAcPTerWw0lWZu3LjRPPTQQwHfV/fbuXOnmTBhQljXCAAAAERtEE07yosXLzY33XTT/y3ovPPs57/88kuy9zt69KhtRly4cGHTuHFjs2LFimRv+++//5rDhw8nuQAAgMhTaaYyy6pUqWLbNwSqQoUK5rrrrrMZbAAAAEBCBtH27t1rTp8+fU4mmT7XjrM/pUuXtllqEydONJ999pntqaISkb/++svv7d944w1z0UUXeS4KvAEAgMhTAO2LL74wH3/8sc1MC4ay0ebMmWNWrlwZtvUBAAAAUV3OGaxatWrZ3iiVK1c2derUMePHj7c9VXRA7s+zzz5rDh065Lkw3QsAAPfcdtttpnr16kHfr2nTpnbKp9o+AAAAAAkXRMubN68dbb9r164k1+tz9ToLxPnnn2/LQjTxy58LLrjADiLwvgAAgNii93NN8xw5cqRt6wAAAAAkVBAtc+bMpmrVqmbWrFme61Seqc+VcRYIlYMuX77cFCxYMIwrBQAAbuvUqZM5cuSI+fLLL91eCgAAABKQ6+Wcjz/+uBkyZIjdWV61apXteXLs2DE7rVNUuqmSTMerr75qZsyYYSd7LVmyxNx7771my5YtpkOHDi5+FwAAINw0VEjloAMGDDBnz551ezkAAABIMJncXkCLFi3Mnj17zIsvvmiHCajX2fTp0z3DBrZu3WondjoOHDhgOnbsaG+bO3dum8n2888/m3Llyrn4XQAAgEjQZlvDhg3NggULzNVXX+32cgAAAJBAMpxNsK3cw4cP2ymdGjJAfzQAAGKL2j5cccUV5tprrzWjRo1yezkAAABIoFiR6+WcAAAAgVJ2eufOnc3YsWPN3r173V4OAAAAEghBNAAAEFPat29v/x0+fLjbSwEAAEACIYgGAABiSt68ec3dd99tBg0aZMs7AQAAgIQYLAAAABCsrl27ms8++8xO7S5evLjn+syZM5umTZvafwEAAIBQYrAAAACIOTp8ue6668z8+fPP+Vr//v3NQw895Mq6AAAAEHsYLAAAAOJWhgwZzNy5c82///6b5NKkSRMzcOBAG2QDAAAAQokgGgAAiNlJnSrb9L506dLFrFixwm+GGgAAAJAeBNEAAEDcuPHGG80VV1xhs9EAAACAUCKIBgAA4oay0zp16mTGjRtn9uzZ4/ZyAAAAEEcIogEAgLjStm1b2zNt+PDhbi8FAAAAcYQgGgAAiCt58+Y1d999t/n444/NmTNn3F4OAAAA4gRBNAAAEHc0YGDjxo1m5syZbi8FAAAAcYIgGgAAiDu1atUyFStWZMAAAAAAQoYgGgAAiDvqiaZstG+//db89ddfbi8HAAAAcYAgGgAAiEutW7c2WbJkMUOHDnV7KQAAAIgDBNEAAEBcypkzpw2kDRkyxJw6dcrt5QAAACDGEUQDAABxq3Pnzubvv/+2ZZ0AAABAehBEAwAAcatKlSqmZs2aZtCgQW4vBQAAADGOIBoAAIhrGjAwY8YMs379ereXAgAAgBhGEA0AAMS15s2bm9y5c5vBgwe7vRQAAADEMIJoAAAgrmXNmtW0bdvWDBs2zJw4ccLt5QAAACBGEUQDAABxr1OnTmbfvn3m66+/dnspAAAAiFEE0QAAQNwrXbq0qVevnhk4cKDbSwEAAECMIogGAAASQufOnc38+fPN8uXL3V4KAAAAYhBBNAAAkBAaN25s8ufPbwYNGuT2UgAAABCDCKIBAICEkDlzZtOhQwfz6aefmqNHj7q9HAAAAMQYgmgAACBhdOzY0QbQvvjiC7eXAgAAgBhDEA0AACSMIkWKmEaNGtkBA2fPnnV7OQAAAIghBNEAAEDCDRj4448/zMKFC91eCgAAAGIIQTQAAJBQGjRoYDPSGDAAAACAYBBEAwAACSVjxozmwQcfNKNHjzYHDhxwezkAAACIEQTRAABAwmnfvr05deqUGTlypNtLAQAAQIwgiAYAABJOgQIFTNOmTW1JJwMGAAAAEAiCaAAAIGEHDKxZs8b8+OOPbi8FAAAAMYAgGgAASEh169Y1pUuXZsAAAAAAAkIQDQAAJKQMGTLYbLTx48ebXbt2ub0cAAAARDmCaAAAIGG1adPGTuscNmyY20sBAABAlCOIBgAAElaePHlMixYtzODBg83p06fdXg4AAACiGEE0AACQ0Lp06WI2b95svvvuO7eXAgAAgChGEA0AACS0GjVqmMqVKzNgAAAAACkiiAYAAEyiDxhQNtqUKVPM1q1b3V4OAAAAohRBNAAAkPDuuecekz17djNkyBC3lwIAAIAoRRANAAAkvBw5cph7773XDB061Pz3339uLwcAAABRiCAaAACAMaZz585m586dZtKkSW4vBQAAAFGIIBoAAIAxplKlSqZ27dpm4MCBbi8FAAAAUYggGgAAgFc22qxZs8zatWvdXgoAAACiDEE0AACA/+/uu+82efLkMYMHD3Z7KQAAAIgyBNEAAAD+vyxZsph27dqZ4cOHm3/++cft5QAAACCKZDh79uxZk0AOHz5sLrroInPo0CGTM2dOt5cDAACizLp160ypUqXMqFGjzH333ef2cgAAAKyDBw96NvkyZsxoLrnkkiRf379/v/n3339TfZxs2bLZuIi3HTt2BLSG3Llz203HRI0VEUQDAADwcfPNN5vjx4+b+fPnu70UAACQ4P777z/z+eefm4ULF3quy5cvn3nllVeS3G7AgAHmzz//TPXxrr/+etOyZcsk13Xv3t2cPn06oP6xlSpVMokaKwq6nHPJkiVm+fLlns8nTpxo7rzzTvPcc8+ZkydPpn3FAAAAUUIHiD///LNZtmyZ20sBAAAJ7MiRI6Zfv35JAmhwT6Zg79CpUyfzzDPPmIoVK5qNGzfa6GWTJk3MV199ZXds9Z8LAAAQy+644w5TsGBBM2jQILurCwAAEGk7d+60xyF79+61n19wwQWmcuXKJkOGDCZHjhzn3L5MmTLmwgsvTPVxixUrds51NWvWNGfOnAmonDORBV3OqfQ2ZaOVKFHCvPXWW2b27Nnmu+++s+UOCqht27bNRDPKOQEAQCBefPFF895775m///7b74EqAABAuKxZs8ZOC3d6oCmO0bVrV1O4cGG3lxaXwlbOqZibE52cOXOmadiwof1Y/5FOdBQAACDWdezY0WbZf/HFF24vBQAAJJClS5eaDz/80BNAK1SokHn66acJoEWBoINo1apVM6+//rr59NNPzY8//mgaNWpkr9+0aZPJnz9/ONYIAAAQcTpQve2228zAgQPtJiIAAEAkFClSxJMNVb58efP4448nfBllzAbR1PNM5ZzdunUzPXv2NFdccYW9fty4caZ27drhWCMAAIBrAwa0G0wzXwAAECm5cuWypZs33nijPRbJkiWL20tCWnuiJefEiRMmY8aM5vzzzzfRjJ5oAAAgUBr1rg3DOnXqmBEjRri9HAAAEKcTODNlymSyZs3q9lIS1uFw9URznDx50vz1119m69at9rJ7926zY8eOtD4cAABA1NEGoSaTjxkzxuzfv9/t5QAAgDiza9cu06dPH/PJJ5/YzTtEt6CDaGvXrjXXXXedjZCqTlejUXUpWrSo3zGpAAAAsaxdu3b2oHbUqFFuLwUAAMQRxVcUQNOQxpUrV5rJkye7vSSkIpNJw4Gk0gz1n1uwYEGTIUOGYB8CAAAgZmhwUtOmTc2gQYPMI488wrEPAABIt19//dV8/vnnnuwzTeC8/vrr3V4WQh1E++OPP8zixYtNmTJlgr0rAABATOrSpYupW7eumTNnjrnhhhvcXg4AAIhRaks/ZcoUM3XqVM91msD5wAMPMEAgHss5y5UrZ1MNAQAAEoV2hrWBqGw0AACAtFJVn3cATcOLmMAZx0G0t956yzz99NN2J3bfvn12goH3BQAAIN6ohFMHuOPHjzc7d+50ezkAACAGzZgxw0ybNs3zebNmzUzz5s3tICPEhgxnlUsYhPPO+1/czbcfiB5G10X7NIlAx5YCAAB4O3DggLnsssvM888/b5577jm3lwMAAGLIokWLzLBhwzyf33333bSIiMFYUdA90X744Yf0rg0AACDm5M6d27Rs2dIMHjzY9OjRg11jAAAQMPU9K1GihNmwYYNp3LgxAbREyUSLdWSiAQCAtPrtt99MjRo1bD+TRo0aub0cAAAQQ/7991/z+++/m6uvvtrtpSCNsaI0BdEOHjxoPvnkE7Nq1SpPRLV9+/b2CaMdQTQAAJBWOmyqVq2aufTSS823337r9nIAAEAUc9pewcRNrOi8tNTxKgXxvffeM/v377eXvn372uuWLFmS3nUDAABE/YABjabfsmWL28sBAABRavXq1aZPnz7myJEjbi8FIRR0Jtp1111nrrjiCjNkyBCTKdP/WqqdOnXKdOjQwWzcuNHMnTvXRDMy0QAAQHocPXrUDhjo3r27ef31191eDgAAiDLr1683/fv3NydPnjT58+c3jz/+uMmRI4fby4JbmWhqpusE0EQfP/300/ZrAAAA8ezCCy809913nxk6dKj577//3F4OAACIIspUHzBggA2gSYECBUy2bNncXhZCJOggmiJyW7duPef6bdu2EVkFAAAJQSWdu3btMhMnTnR7KQAAIEr8/fffNgPtxIkT9vOyZcva/vFM9E7gIFqLFi3MAw88YMaMGWMDZ7qMHj3alnPec8894VklAABAFKlQoYK59tprzcCBA91eCgAAiALaXPvggw/MsWPH7Odqg9WpUydz/vnnu700hND/1WQG6J133rFNddu0aWN7oYl+Kbp06WLefPPNUK4NAAAgqrPR7r33XtvS4oILLki1p+wtt9wSsbUBQEp++uknM2PGjDTdN3fu3KZr164mS5YsIV8XEKv27dtn3n//fdtXS4oWLWr/TjJnzuz20uD2YAHH8ePHzYYNG+zHmswZKzW+DBYAAAChoFKNG2+80Wzfvj3F22kq13nnnWez9znpBOC206dP2/M3nQ/pvChYei1T8sRTTz0VlvUBsebgwYPmvffeM3v27LGfa/jQo48+arJnz+720hCGWFHQmWgOBc0qVqyY1rsDAADENAXE5s+fn+rt1q5da0qXLm2++uorO5AAANw0depU2/h84cKFpnr16kHf/8EHHzR9+vQxDz30UMwkUgDh9OOPP3oCaJrE+fDDDxNAS/RMtKZNm5oRI0bYaJw+Tsn48eNNNCMTDQAARJpKOXUM8uuvv7q9FAAJrkGDBmb//v02iJYWmzdvNiVLljRvvfWWefzxx0O+PiDWnDlzxnz55Zdm9erV5oknnjC5cuVye0lwOxNND6Q+aKIHcz4GAABA6tQXpUmTJmbx4sWmatWqbi8HQIJat26d+e6778zIkSPT/Bjq9aT+2G+//bbti501a9aQrhGINWrZ0KpVK3P06FGTI0cOt5eDaO2JFqvIRAMAAJGmYUzFixc3N910kxk2bJjbywGQoB577DHz2WefpbtHo3pjq0z93XffNY888khI1whEu5MnT9p4Qr58+dxeClyIFZ0X7APXq1fPNs7z94T6GgAAAJLKlCmTneapcg9N8AKASDt27JgZPny4eeCBB9I95ESDCTSdWCWdGrICJNKm2JAhQ2xfwL/++svt5cAFQQfR5syZYyOvvvTiqVHJAAAAOFeHDh1s3xT1mQWASPviiy9s4oMC+qHQs2dPs2vXLjN06NCQPB4QC5NtFYhesWKFLd0cNGiQDaohsQRczrls2TL7b+XKlc3s2bNNnjx5kvwyTZ8+3Xz88ce20WQ0o5wTAAC4RZkbGi6giZ3qoQIAkaBTvipVqpgiRYqYiRMnhuxxNXH4hx9+sOWdF1xwQcgeF4g22gQbNWqUZyDH+eefb7p162aHbCA+BBorCjiIpgM9Z6CAv7uooeSHH35o2rdvb6IZQTQAAOCWn3/+2VxzzTVm6tSp5tZbb3V7OQASxLx588x1111nhwpoWnCoaBphuXLlzEcffWSHDADxSPGP0aNHeyrvnBYN+t1H/Ah5EG3Lli32l0dNcRV99W6ilzlzZnPJJZeYjBkzmmhHEA0AALhFx1JXXXWVueyyy8zkyZPdXg6ABHHPPffY6cAKeoU6C1ZTCRWkW79+vT0vBOLtfXv8+PFm1qxZ9nP9/ag9gyr0EF8CjRVlCvQBlfrrpDECAAAgeMrqf+ihh8yDDz5oT2Yvv/zysD2XDvTT2zwcQOzbuXOn+frrr83bb78dljLy559/3lSoUMH2e9RrGxDt1FZh0aJF58Q2lE2pMk3v7PH58+ebTZs2ed7D77//fgJoCS6gINqkSZNsyYF+ofRxSu64445QrQ0AACDuKGvjqaeeMmXLlg37c6n3UTQem61Zs8ZcffXVtrSsRo0abi8HiFn16tUzNWvWNG+88Uayt9EkQZ3HtW3bNixrUEnb3XffbXr16mXatGlD8B5RH0BTbzN/fIv09uzZ4wmgOe/f1atXD/saEd0CKufUjoV2MFSymdLuhSKzGjIQzSjnBAAAbvv999/NqlWrwvoc/fr1s6VVKrOKNtrt11QznXiPHTvW7eUAMfs6ovLwXLlymb///tv2qPb133//maJFi5rbbrvNDoELZ2Bc2WivvfaaeeaZZ8L2PEB6rFy50gwYMCDZ6jrnfdN7I0qbPdmyZTONGze2fQURv0LeEy1eEEQDAACJ4JtvvjFNmza1JStVq1Y10WL//v2mUKFC9sReJ94bN270tA0BELiuXbvaZucHDhwwn376qZ3+62vcuHE2WL106VJTqVKlsK7nkUceMcOHDzfr1q0z+fPnD+tzAcHaunWree+998y///5rP7/++uvNnXfemeQ2mjDrDFN0gtBKElImZyz0f0dkYkUhKYo/ePBgKB4GAAAAIaIyTgWq3n//fRNNVFqmk5IpU6bYg9T+/fu7vSQg5hw7dsx8/vnnNpB2ww032L8rfzQ189prrw17AE1efPFFO7XwpZdeCvtzAcHYt2+fzUBzAmhXXnmlad68uS099r54B9BEwTNdTwAN6QqivfXWW2bMmDGez7WzkSdPHjtlSjscAAAAcJ8O+rt162YzVdSWIxpoV19Bs9atW5tixYrZJuQ6+T969KjbSwNiyldffWWzJh544AHTsWNHM3fuXLN27dokt1mxYoWZM2eOfR2IhIsvvtgG0vQ3vXz58og8JxCICy+80BQuXNh+XKJECdOuXbuwDNlAYgj6N0f9K5xfwO+//97MnDnTTJ8+3Q4eUJNcAAAARAedYKu/i47foqXE9K+//rJlX6KTewXQNNUPQOAGDx5sbr75ZhuMbtKkicmdO7f55JNPzslCK1CggP16pCgzTkGKJ5544pwm7YBbVKbZuXNn07BhQ/uvd98zIOxBNO1kOkG0yZMn2zTIW265xTz99NPmt99+C3oBAAAACA81HL///vvNwIEDPWUsblLT5rp169pSGtEx5V133WVLTpNr9AzAnJNh9ssvv9gMNFG52X333WeD0SdPnrTXKUtNfdKU7RnJgIGeq0+fPjbZYtq0aRF7XiCQ7GwN2MiePbvbS0GiBdG0y7Ft2zb7sTLQbrrpJvuxdhqifTInAABAonn44YfN7t27k7TjcMOCBQvsif+jjz6a5Hp9vn79ejN16lTX1gbEEpVL5suXz04LdHTo0MH+nSvJQUaNGmX++ecfG0Rzox+jguXKRlMJNxBpik0oiKtBNoDrQTRNeWrVqpVNH1aDPpVxOiOWr7jiipAvEAAAAGlXunRp06BBA5vt5WZ5lZ6/ePHiNhPA29VXX21q1qxps9QApOzEiRM2w0wZpt4ZZhUrVrR/Rwqw6e9cpZw6b1Pf6khTc/a+ffva6bsqOwUiTQG0b7/91rzzzjtm+/btbi8HiR5E01hY9a8oV66cTdNVkz7ZsWOHrYEHAABAdFEPsiVLlpj58+e78vw6iVEjdGXF+Ztypmy0WbNmmWXLlrmyPiBWjB8/3mbXKPPMl8o7v/vuO1vWuXr1avPQQw8Zt1SpUsW0bdvWTuo8ePCga+tA4tH7nJORqd89xSmAUMpwNsE6Pqo/wEUXXWQOHTpkx6oDAADEO/Ub0wZohQoVzLhx4yL+/D179jQffvihHSrg7/hLJV/KUlOfXd/m6AD+zw033GAzzTR105eGdBQsWND+PalCSBMylRXmlr///tuUKlXKNnJXRhAQbvqd//jjjz09NpWN6bSfAkIVK0rTXNcNGzaY7t27219IXbSruHHjxrQ8FAAAAMLsvPPOs8drmo65ZcuWiD738ePH7UlN+/btkz0oPf/8822lw+eff277OgGxRH2ilU3pfVH1TqitXbvWBs+cgQK+VCHUsmVLO0REWWhuBtDk0ksvNT169DAffPCB7XsIhJPe27QJ4wTQ6tWrRwANYRF0EE0pwtrJXLhwoalUqZK9qFGsU94JAACA6NOmTRsbxFKvpEhSYEzlZ9qATYkCAyr1HDBgQMTWBqSXBquptFKDO2bOnGkvU6ZMMY8//rjNigmloUOH2iFvzZo1S/Y2jz32mGnYsKGd1hkNNFwgf/785rnnnnN7KYhj6tWuKdTOdNqqVavaLDQgKso5Vd9ev3598+abbya5/plnnjEzZsyw/TaiGeWcAAAgUT311FP2RFxlldmzZw/78+kwUyWkJUuWNBMmTEj19spGUzBi69atJmvWrGFfH5BeSiJQGfKvv/5qG/uLTuSVhdWuXTvTp0+fkDyPHrNQoULmnnvusUM6Yol6tOlnoem8GiQChJKm0L777ru2fFhKlChhM6+V4QxERTnnqlWrzAMPPHDO9UrRX7lyZbAPBwAAgAhRkEoHiZruFwnKytHxocrbAqHbKaMgUusD0mvkyJF2Am6NGjU812lqpoJdn332mTl16lRInmfSpElmz549yZZyRjNlxal66cknn3R1QjDiMxNUG0NOAO2SSy4xnTp1IoCGsAo6iJYvXz7zxx9/nHO9rtMvLQAAAKJTkSJFzJ133ml7FEXiZLZfv37myiuvNHXq1Ano9mqGrvX17dvX09cGiFYKSGtapqZQ+vYfu//++83OnTttIDkUhgwZYrO4lNkZa1SmrYw8TU2cOHGi28tBHNHf3WWXXWY/VnZ1165dbW9AIJwyBXsH7X48+OCDdpBA7dq17XV6QXzrrbds7T8AAACi1yOPPGKDWk4ZWrioCfrUqVPNsGHDgmpwrmyVa665xt73tttuC9v6gPT66quvzIkTJ8y99957ztfUk6ls2bJm1KhRpkGDBul6ns2bN9u/V2XcxCq91tx888120ECjRo3IFELIhuao95kSfTSZlqQeRGVPNN1cu4redceq+VePDdUeuz0FJjX0RAMAAIlMx3JXXXWVPX5TA/Rwlo6OHTvW9jfLkiVLUPetVauWvc8PP/wQtvUB6XX99dfb31P1hfZHSQYvv/yy2bVrV7rOO1544QXbB23Hjh0R6WUYLkuXLrX9tfv3728zhgAgIXqiKUimqS9qSKsH10Ufa1cz2gNoAAAAiU7HazpuU6aXssXC4cCBA2b48OGmS5cuQQfQnIl+c+bMMYsXLw7L+oD02rBhg/npp59s2WZyWrdubf7991+bsZZW6qmmbE49ViwH0ESl3ZoSrMDikSNH3F4OYpQyMzdt2uT2MpDAgg6iOXbv3m37oOmiJpfpoVHrRYsWtQdZmmqzcOHCgO43evRoeyCo3hkAAAAITMuWLW35i3qjhcMnn3xi/vvvPxtES4smTZqYYsWK2coHIBqpTDNHjhz2dzU5mqZ500032dumlYLdqv6JxYEC/rz22ms2gPb222+7vRTEoL1795oBAwbYyrglS5a4vRwkqKCDaHrR04QVlQCon4Yu+li9AJSVFiyNMVcvtZdeesn+IWiHon79+jZIl1oEWj0zrrvuuqCfEwAAIJFp47Jz585mxIgR5uDBgyF9bGXOfPjhh3Y6YYECBdLciFyTOp1yUCCaaOiFAmPNmzc32bJlS/G2yryaO3dumjNnNFBA5de6xIPChQvbv20FyLdv3+72chBDjh8/bpNvjh49ajdplAnKtFfERBCtQ4cOZsGCBbaHhg66dJk8ebJZtGiRHScbLE1f0s5Ku3btTLly5cygQYPsm5HSllMaZauU5ldeecUUL1486OcEAABIdMoSO3nyZIrHXGmh6XsKfKlkND3at29vM33ClS0HpJVO3rWhn1Ipp0OZapoW+Omnnwb9PGqZo0y0eMlCczzzzDP2fE9JFECgmzMKKKu/oOTPn9/+XdBOCjExWEC1+N9995259tprz3kz0eSZY8eOBfxYOnDTC+i4ceOSlGTqDUnBueRGIOsFd9myZeabb76xI6V12wkTJgT0nAwW+F9D4UmTJtl+JYHsoAEAgPikSgJNWf/ll1/slLNgrFu3zt5XGQHe1qxZY0tudPKfXs8++6wt3dm2bVvCHrch8llmOlZWNmRKAd4ff/zRrF+/PqCTeCUL6FxJfy/BnPQrgPzee+/ZgQLx9vuv7019tjVsoEKFCm4vByGmvyGdywfad/Ouu+5KkhyjzE3vXoL//POPJ4CmoPTTTz9t8ubNG4aVI5EdDjBWlCnYB7744ovtA/vSdblz5w7qsXSApawyRZK96fPVq1f7vc+8efNsnw31YguEmnnq4v2DSXQ66FUgVC9Ay5cvt33oAABA4lFZ1eeff24KFiwY1P0yZcpk23v4Gxqg44tcuXKZEydOpGmogLfu3bvbsq+hQ4fa9h9AuD300EP2xH/WrFl+v66EAZ3cP/XUUwEHxJQgoNLptJQ3K2AXbwE0UTm5AmkKhoQi4I7oor+NEiVK2FJmvRekRkEy38+V7enr/PPPt1nUBNDgpqCDaM8//7w9iFFKsvNGsHPnTvtGovHL4eT0Y1MqZ6B/OG+88YYt+8T/2bdvn/1X9eS+L1gAACBxVKtWzW5Q7t+/P6j7aYNSgQaVmyVXevPrr7+aunXrpmt96rur3mrvv/++DajpBAoIFx0b6xxHgbJVq1aZsmXLnnOb8ePH29up11mg1EP6hx9+CHoipQIRvtU/8SJz5sz2PE1VMQpY3njjjW4vCSFWsWJF+3cyePDgkDxe1qxZbSxAQ2eAmCrnrFKlik1d1sHT5Zdfbq9T34sLLrjAlCxZMsltU5uYEWw5p7LP9Pze6dVKuRaVIKh8QBHv1DLR1NAykcs5p02bZr799lv78YMPPmgqV67s9pIAAEAMcho8O9UICqr17t3bfnzJJZeYF198MegyUV8q99KxyhdffGEDakC4KFvM6cWnTKm33nrrnNto2qaCxHPmzHFljfFEp6G1a9e252rqr53e1woAiMpyTu9gVyh2IKpWrWp3H5zHVVBMn3fr1u2c25cpU8aWH/pmxmlXRzuUCo75UnBPF/wf7ylcKrcAAABIC5VueitUqJApVaqUzVJTdtvff/9tr0sPTW5X4EJlnS1btqSRNMIaRKtXr54pX768nb7Zq1cvW7rsUG++2bNnh3wYR6LS33KfPn3MddddZ7788ks7OA6xSxmcSnpRYJTXacSzoINooZ6iotJQZZ6pnKBGjRqmX79+9g9QDThFKaCXXXaZTfdVXw3fxpNOEIiGlIEjiAYAAMKlfv36pnTp0rYMTRk9ofDEE0+YW2+91fbXUWkcEGpqZK5hASrn1HmF+nVNnz7d3HbbbZ7b6GsqKWvWrJmra40nep1QMsVzzz1nf67p7aMId6jPufqWq6/5hg0bbNYw5feIV67nzLZo0cK88847Nt1fqfqKXusNyxk2oFJRTaRBeIJo6mOnsloAAIBA+qoq0JBSo2j1kVLAK1QBNCcwp8CGstGAcFDmmTIrmzRpYs9J1ELGO+NMpYfKVFOgJ5S/2zDmzTffNNu3bzcffvih20tBGqkNkzMY8M8//7Sl/kC8CronWqLUucazHj16JGls+vLLL9u+JQAAACn5+uuvbdsNZeN07NjRttqIlOHDh9t+VTpRU6YbECpqJ3PFFVeYG264wWbTiAI6qphRSXK+fPnML7/8YsvUZs6cSRP8ME1F1aRgZTFdfPHFbi8HQVA/OyfgrL52jzzyyDm90oF4ihW5nomGyFIjVN/JQN6ZaQAAAP4o++znn3+2H2uYgNptBHq/lDLXAtWqVSs7Gf69995L92MB3n766Sdbztm2bdskv28KCHz22Wf285EjR9r+ywq0IfRUlaSSQPWhQ+zQMBmVOTvuuusuAmiIewTREoyiqoFcBwAA4G3hwoXmn3/+sR9Xr1491ZI27eiOHz/e9OzZMySTDDUoSoOnFMzYs2dPuh8PcOh3qnjx4rY/l0PZUI0bN7YZkAoCjxkzxvZqZoJkeKiVj6pl+vfvbzZu3Oj2chAAlWx+/PHHdlNFrr76anpWIiEE/S7www8/hGcliAh/WWcHDhxwZS0AACA2qPuH9zFg3bp1U72Peq6q9FOBN/VRUzZ8enXu3NlOfRswYEC6HwtwAgFjx461g858JwqqfHj58uU2S0rH0AqiIXxUPqvSWQXeEd2UNagSTvXJlCJFithhAkzlRCIIejpngwYN7KhyTc/Um43SmhE7MmfObBul/v77757rtNuzbNkykzt3bv4/AQDAOVatWmV27dplP1bvqECOF/LmzWsqVapkli5darPedeyhDLb0UHaQjkE/+ugj8/TTT9vebIlGwcn169ebEiVK2Oy8YOh4z9+xoR4rUSfpKVvy2LFjfgNkN998sy1b7tOnj6lVq5YpVaqUK2tMFNmyZTOvvvqq6dChgw2opff1IlaptNi7/Y4CUxUrVjynjHL//v3JPoaGZBQrVixNQS31CNRrjHcZvl7zda7omDZtmmeQgLKSH3zwwYR9DUHiCXqwwN69e23ds9KeV6xYYerVq2ceeOABO5pYb8LRjsEC/6NdA03m9KYUXHbYAACAL2V+aeKaaKCANuQCsXbtWtOvXz/78eWXX27LtdKbqaCTOwUzVEaktcR7wMz3+Fonzs8//7ztO/Too48G9fNUOaxOkH1pGqVOghMh6+zZZ581x48f91w3d+5cmyCQXLWNsqJ69+5tf98S4WcUDRlO+n1U5qoGOQTjmmuusdmDsWz27Nlm3LhxSa5TsNy3F6SGMMyfPz/Fx7r99tvtpORgffHFF2bevHlJrtPmhRPU1GuIkjD69u1ry5v1OqTNFSDWhW2wgHYVH3vsMfPHH3+YBQsW2IOYrl27mksvvdQ8/PDDdrcR0S9XrlxJdhMAAAD82b17tyeApmMHZZcFSoEeBShk69atIel1pJM19ap6//33bZlpvFIQQRPUFbxZs2aN53plTcm6deuS/Xkqu0on2du2bQvouXRcnwh95hQcUEB45cqVnovKB5977rlk79OlSxdz9913m5YtW0Z0rYkqY8aMZsiQIeaSSy5J8v+U2kUTIpXY4Rv8ibXX2okTJ4bs8SZPnmynnQZj8eLFqf4MFTjLlOl/BW362yCAhkQTdDmnt6uuuspOSVJq/ZtvvmnrovXGpHTnQYMGmfLly4dupUi3HTt22HTb7Nmz2zcojR9W0FM7PhLolC0AAJA41M/MoabROoYIlLKkVLUwatQo+7myfVQ6mF46htGURGVt3HjjjSYeKbClPly66KS1dOnSnlIv7/8b35+nSrA0bfLff/+1gYU33njDZMmSxX7tjjvuSJKJpiCb0+JDWS2qLIlnmrR5yy232FK0QCkIrJ5piBxVx3i/7gRC5zPKXFPfRP1Ox1ppof4uFfh2mvRrs6Jo0aL2Y3+vucrW0zl4cud8v/32m3393b59e8CvuapUUqDZcf3119vEC3/nibpeP+tgNlWAhA6i6Y9bUXIFzb7//ntTrVo1O0lFzQS1i6UUc0WltSuA6Hlh1sho/avGjyqn0A6Pej347oDo4EzDBvT/qsxDAACQmHRiqpMx0UlpsOVVUrVqVfPNN9/YHj8KDKkcMU+ePOlal4J56hH0wQcfxG0QTWWG3iezDm1WK8NEpYlLliwxzZo1s+UnDv1/KYAmOpZzAmiiAJJv6Yo2VHV8+Ouvv9ryr2CCpLFk8+bNNriotjSIP/q9VRKHfudV+qieibFEQWxll3r3fkyp56GSVZJLWNHrtgJoep1UX7RA6D4jRozwTGDWz7FFixbJlosriOYE2IBEE3Q5Z/fu3U3BggVNp06dbCmnIv2//PKLbQCpDCdFzN955x1Po0FEBx24OjuPajSZHB3cqt/dpEmTzJYtWyK4QgAAEI0npuqhquCKMspSOoZIjoJv1113nf1YxyLBZpj4oxM7tRH59ttvQ1IiGm3UNFy930TH3d4N7fXzVO8n5+fpXXql8lYFivwF3/xRz5crr7zSE1DTJMp4pQwbNa6P92y7RKZejcpSVRm0gqaxQskL2mhwtG7dOuihIb6v223btg04gCbKznRKPxXEY9ImEMIgmrLLPvzwQ/P333/bRrEVKlQ45zbKXkquOSfcoeZ4jpR6oXl/LaWJLwAAIDGoFYSaU6sPWVopiOb00FHQx8mUSg+daOq4RdUQ8Z6F5nsyq5+nc52CZuqfJqomUABOtLEdyBRVBeTUF0zBpeLFi5t4pOCiSjmbNGmSpkAwYscrr7xig0AaohErPRMVvHYmYSrTtEyZMmF5Hn9DRZwg3nfffWc/Vum4suAScfIxELYg2ksvvWRLNX2j43rzdt7wdZCk9FFED/XTcKSUeutdXkEQDQAAhILKDVUepMwnlV8mdzIXDJ3kaVriJ598Yksb44UmRy5cuNB+rFLMmjVr+j1e884gUyVBSiWgKdEJuzJ3VOoZr5Pr9fNZtWqVDbwi/oP+KvOeMmVKkuyuaKa/VWXQqXeZyrNDTaWaU6dOtcNYnF7Y3rQZoefXv40aNYrbYDrgWhBNTVz9BVeU6aSvITpph8Hh3TfDl3eDSoJoAAAkLie7KVSaNm1qXnvtNdOwYcOQZTloQrymVTqDC+KBepOdPHnSfqwAmndPM2/eG9YqkXV6pInKFjUALBDKPIn3si1loSnbzrcXMOKTsipVgq6Sb7W0iQUaHPLEE0/Yv91Q0+uj+iiq55qTceZLEzZ79uxp6tevH/LnB0yiB9GUFuvvjVbTPNQTDdGfiZZSOad2IJ2GsgTRAABITDt37jTPPvusGT9+vD3GCwWV0YV6Yp7KFRWcU+ZJKLLb3KbvIdBsMvVJU780US+jcePGeQKfmm6YOXPmCKw4+inzRv3Q1OPJKSlGfNO5qtoPKYngxRdfNInOuyRcGWnJ9ZFUAE9BdQApC/ivRAcouugPUI0Knc91UY8MRa3TMrEJke+JllI5p144nSAbQTQAABKTMpuU4TVz5kw7RCqaKdtkzZo1dmJ8rNNgLk1KdzJTnCCZP870PVGWlVMCKs4gh7QET7/++uu4GhA2e/Zs+31RyplYihQpYsuUFWCPxtcwlRerP2Qkgv8qE1VfS9HzDR8+3A5e0GtmPGw+AJEW8HaMUwKoTDTVmnun4WunSzteHTt2DM8qEdJyztTGEavPxt69e+2IY11oLAkAQOLQe79KCp1jvHBskmqjToE6HT+mFCgKhBrjayqfTpZjvRRJ1QA6+deE9EB6mtWoUcMO9CpQoIAd+KXjNwXf8ufPH/Rzr1271j6G6HHC1dw80j7//HNTsmRJU716dbeXggh79NFHzaeffmo6depkfvnlF0+1jds0REC/l3od/O233+z6wlHG6U1BNAXHlYWm7OJ33nnHBtA0NLB9+/b2/B5AiINoilg7E06UHstkm9gs59TBcGpBMe/hAnqRvfTSS83YsWNtNpt28fi/BwAgPiiDa+LEiUmmZepj53P15Ar1yZ0m0X388cf2BG7BggXJtgNRiwk1u/amkkVlcPhSX97t27eb559/3g6/qly5su2J5O2tt97y9BpLiRp7lytXzoTS9OnTzaJFi5KdFqgeThUrVrQBsB49etgskUAma6pfmrNWZd3oJNl3+Fcw2SraNNfxnv6P9G9KfXRD7b///jNbt25N8TYqB9bPJbkebvr/3bZtW5LHVGbdU089Ffd93+D/92XQoEE20P7uu+8m27Rfv+cKRgdD2W3qL6bfMdHvpaq1vA0dOtTs2LHD70aFc26mKqBIJCwogKj19e7d2wbxnAw0DSZJ62sGkKiCagygN35FzZ977jm7o4PYK+dUFlpqBxG+Ezp37drl6c+hA1r1lAAAALFPzaYVsElO3bp1Q/6camCtTT2dyOkEThd/dKLpL7Pe30mpc/zitKIoVqzYOV/X8YyeMzWB3CYYyiqbNGlSUM9ZtGjRoJ9HJ+PpCf7pJFuZgQoM6ARb2YiRzOzr3r27Da6mRkExtZPxp3Pnznbj35uOe1u1ahWydSK2KJNWU3wVnNbFH70e/fTTTzazMxBKMhg5cmSSoLy/zQBldCb3euUE+ZSgEKkArwKFLVq0sGsX9QhUFhr9E4EwBtH05qzgmV44CKLFll69etkdD2e3JLUXWB2I6qIXd+1GOvQGQxANAID4KXdSsESbpN5TIBVQUV+t9JZa+qOsi7vuussGllLKDPOXHaHjkuSmVToZdCpL8jfAINBsC9/G2goopafZ9g8//OD5WCer/h4rWpp5K2PHmd73888/22mWkVjb8ePHbfN/BTtatmyZ7O2UUabsHn9BNE0nHTNmjHnooYeSZBxp8rwCt0hcAwYMMPfee2+yE4c1FVMBWPUVDGT4xFdffeV57dJrjV4v/QWidF1yr1f6mn6Pg82ASy9lF6tHoDJj1df8sssui+jzA/Egw9nk8sqT8e2335q3337bDBw40FSoUMHEGu12OqnqyqpC6nRQo8aX3m9EAAAA0eTvv/+2/cRUtqVhA+mlTWNl4qtnkaow0tLOQsedPXv2tBMilamiTc1oz/pQbzlnsIDKaVViGm6jR4+2m7Tqy5bSRv3gwYNNly5dbMmm2o14GzVqlLn//vvNpk2b0pTJh8Sl4JmyMNUTMLXXjmXLltkSUdG55EsvvUT/aCBOBBorCnprqU2bNvaF5sorr7QvGE7GknNB/NHuIAAAQDRTUOXuu++2vXtDMXFOGWSaXqdM/vnz56fpMRR4U4aLyiyvvfbaqA+gOdlojrR+38H67LPPbBAjtUqX5s2b28wfZU76UgN5TSslgIZgqYxTzf3VU1HB+OQo01V9oh3KqCWABiSeoMo5xZnag8RRvnx5s2TJEs/n2k2Nluk2AAAgeCp9i8dBQcoiqVWrlpk2bZpp1KhRuh5L0zEVSFPRhtpZ3HTTTUEf/6gUUsdRuoQisBcJlSpVsr8b+h35448/wv67snv3bjt4QRlwqVFv3yZNmpgRI0aYJ5980tNLSkMlZs2aZYYMGRK2dSK+qeH++PHjzWOPPWbLgv2ZOnWqp++iptdWrVo1wqsEEJNBNKVJI7ZodLGa2urAQyW4wY4w1sGoUpeXLl1qP9eOrPpLAACA2CxXeOGFF0zZsmVtYCie+kWp30/16tVtQCa9QbRLLrnEZpCtWLHCnjirR6ymfqZVtPQ9S40yvfRzVFBKPaRUgVKvXr2wlnLqZ6OG54HQhMEGDRrYnk76v3Zaj6jnnTKDgLTInTu3LQW/7777zAMPPGBuueWWJF/fs2eP/ZsQ9U1T7z4mvgKJKV3v5t5TlVKargR3KQCmXnZKc1d/j0CndfXp08f2ANEwAvUYKVWqlA2oAQCA2KWsKr236/hAl3iik1r18ZoxY4ZZtWpVuh/Pezrpjz/+aBKFd0mns4kaLjo+bdiwYcAbtAr8qnTXmTCoTEH1Q7vjjjtsLxsgrTQp84YbbrDDKXwn5moAgJJJ1CdJATYF2QEkpqCDaMeOHTPdunWzLxxqkKqovfcF0UeZYw5lowVCU1vUmFX31Th57fhpgpd2Z8hCAwAgNil4piCaE3BSD6l4o75oBQoUsL3R0kvZes7J8po1a1Lsl+Rt48aN5v3337cBqFgp4/Smn5+CVR07dgzJkIbkaICBMsp0fBkoldTq9so+U48q/Yz//PPPoB4D8EeviRqgpgqeN99885yvKfPxxRdfNPXr13dtjQBiMIj29NNPm9mzZ9vpnEqb1pjpV155xe4IaRcI0RtE04t/oKWc3kMiFFADAACxTz1OncoBlSbG48aYmvermb8ylbw3EtNCZYbqjRZsNtqcOXNs0O3jjz82ixcvNrGoadOmpkqVKmHtg6ssNG3w3nbbbUHdTxlB2uR1Ki3y5ctHYAMhoV5nOt9944037LRYX9myZbMlzwASV9BBNL1ZKULfrFkzWw9+3XXX2Ukmasbob1IO3OccQCrFPdADIe+Dao1x1pvJ77//HrY1AgCA8FLZmxrlO1S2FK8URFPW3SeffJLux1IrC20ci/qD/fPPP6kedzkDmdSQPz191OKZMvQ0lVMTN52fbzAZgurbpv9fZaTdc889BDYQMj179jSXXXaZLesMNPsUQOIIerCAGqsWL17cfqyacGdCicZ2d+nSJfQrRJps3brVZpDpgPnIkSNBlXL6ZqKJJjM5BziRnM65d+9eWxKR2i5xtWrVIrIeAABild5PdXwghQsXNiVKlDDxKn/+/Lbxd//+/W07ivQct2TNmtUGbObOnWvLB3/55RdPo/2TJ0/aCZa+A52cEk5tNsdDcEfHgRqs4P1zVMnn5ZdfnubHVFmxfh/TWoapbLSuXbvajynlRCjpb16vHe3btzevv/66ueqqq+zQimDOpQDEr6CDaAqgqVeW3jSV7jp27FhTo0YNm6HGC0v0+O233zwTZBzpCaKJxj3r4FEZiHpDCTft5L722mt2Jzm1sg2CaAAApMw3Cy3eJ8t1797dlvppSqd3w3ln+EAww5LUO05BNG1OjhgxwpZpOo/l75jJ2eRTEC2W6ftV8Gz+/Pn2X2/63hWgLFmyZECP9eqrr9pJpw59XLRoUVO7du00rU1BUj2/zk2qVq2apscAkqPyYA2rEGWWatMhnrN3AYQxiNauXTvbwFMHE88884y5/fbbbaRegY6+ffsG+3CIoGBG2KuJrg44Dx06ZD/XAaKCVRrvrIMmZaOpvHP37t229LNixYp210aDJ3TQqH4B6bVu3bpUA2gAACB1qhxwMqZUSZAIQQc1AX/88cdt8Ef9s7wzxbZv3+4ZsBCIggUL2inlTzzxhM1CcwKQOuZJLoim7LVY32DW96lNWR2T+QuwqUdcIEE03f+ll16yG+9OQFP9lHVeoZ9hWmigmZq/KxAX7wFhuLPpoMQBh3dvRACJLcNZvQOmw+bNm210XgGaSpUqmWinZrpOcEgHkfFq/fr15q+//kpyoFGhQoWgyhn27dvn2THUfb/++mtPXzTtJupzZ+x5ixYtzIQJE2ymmhrRaqJTKEo5NZ5eB2jqS1CsWLFUm/7q11knCvq/jYfyCQAAQkHv0TNmzLAfN2zYMOhG7vFk/PjxtrevGv6rTCsQOr5Q4FEljFOnTvVcr+MelXxpk/HZZ5/1BHO0saim/NqAjIdSzmXLltnSVYd+BrpeQQYFslLbPH3hhRfstNQdO3bYnw0QzXQuoWoY/X07rxlTpkxJiM0HIJEdDjBWFHQmmi/t/uiC6KKgZjCZZ/4ow8x718V72IACbE4/PAWxtJvovNHoa6GQN29eWwYRaCmETg6mT59uTpw4YXeK47nXCwAAaTle27ZtW8yXGKaXSrTUlkRBneHDhwd0H/VB00aidwBN1C+2Q4cONjtNFRrxWO6l4Qi+JZeqRNAE0lOnTtmfyzXXXJPs/dUfTmW1GiBAAA2xYNy4cZ7zGvX9VjmzhpX8+uuvEesLDSB6pSl/Wmnd2sFUkEIXfTxz5szQrw5B0QHNN998Y2bPnu1pHBxKvkE0ZYo5WW4aLe79NTdot1cBNNFOJwAA+B9NiNSkbZXUefcHS0TKntLUPU111LFTIBRw0+ak+iT5qlu3rs3Y/+CDD0yiUKmqY8GCBSneVmWzW7ZsMW3atInAyoD0+fPPPz2l7zly5DB33nmnGTRokFm0aJGnFyKAxBZ0EG3AgAGmQYMG9kVFTVl1UaqbSgM++uij8KwSAVF/j++//97unqjfR6h59/xQqagz4l3BNf0OOH0D3AqiKRvOwThqAAD8Z3nD2OwxZZQMHjw41dtqY07HVgq8+evfpRLOhx9+2EyaNMkO30oEyuTTBFSdD+hjZxqpPyNHjrTN/1PKVgOigUqWNUjNoRY1KlXWEJKOHTvaku2dO3e6ukYAMRhE6927t3nvvffMl19+aQ8YdNFOnq7T1+BuDa8jHLvM3gfea9eu9XysIJp3Y10F0dLZas9mlK1Zs8ZO6Az0sbyDaAooAgAA+KNjlvvuu89uDqc2xEjZJyrbbNu2bbK3ad26tR0ioMdLBAocKqioY/+77ror2eEAx48fN1999ZXNQqP5P6LdtGnTPMkAGiSiQRgO9f5T1YtaxgBIbEEH0RTUUCaar1tuucUzyRHu0P9NOINo3plo3pleTpmnE2TTLo6azaaHylHff/9989xzz9kBBoH27HAaAGp96Q3kAQAQ67777js7GZH3xHN1797dZpmldJyhYxoF0RRwS2nSprJVlN02dOhQO6k8Eei4L7X+UBpooWNC/fyAaKbXSOf8Rr/XLVu2TBL41XlQnz59bPKIWhsBSFznpaUZq/pu+Zo4cWJCT3pKhEw07cIqUOXLCaJ5B9nSW9Lp3aPEu99aoNloOoA9cuRIutYAAEAs03upSgxVLUAvn3Opj9mNN95oN+2SowCbyre6deuW6uN17drVHot99tlnIV5p7FIppwZZqJwTiGYKmGl4gILh6oOmSby+7r//fjt0TX/rzuABAIkn6CBauXLlTK9evUyjRo3M66+/bi8Knuk6p6mqc0FkeWcChqtpsL9eKr6ZaKEOol1yySUB34++aAAA/I+mJzoZaAQx/FNbEk3cW7hwod+v9+/f307eLF++fKqPpemnjRs3tkMIEi3zT8egasPhTa01NHiMgQKIpUDaVVddZYPryX1dJdsbN240b7/9dsTXByA6/K8TfBA++eQTO41Rjeu9m9crxV1f822yisgH0ZSCnD179rA8h0p5Nc5c/S2c53OCZ77TO0MVRFPj2rQG0cqUKZOudQAAEIs0/OeXX36xH59//vk0dU+GNoWLFStmA1+ffvppkq8tWbLE/Pzzz2b8+PFBlYgq6PbDDz/Yf+OdgoUqYdU0Q5W0vvHGG55BUyp7Uw+pu+++2+1lAiGjgLr6oimBpFWrVqZEiRJuLwlAtAfREmXqUKxRjb76ejhZaOFq3lqpUiVPgEo9VhRIc/qQpRRE+/HHH83s2bPN6dOnPWXB3s069+zZk6ScwilN1YF/Sj1IUgqi7d+/Pw3fIQAA0WHbtm22NNBfj61XXnklST+q6dOnm3nz5nk+V7N8p9yoZs2aYdtci3X6GSrw1aNHD9OpUyc7bdKhTBNNnrz99tsDfry6det6KjMSIYim4039DBVM0+/plClTbEaejBgxwpbFhas6AgiEJsdquu7y5cv9ZojqfKFdu3amevXqAT/mCy+8YEaPHm3LOsORkaZBHar+Sq3nIIAYCaIheocKOOPFvXuThYu//nfqHaBJTXp+74CaDuTVU0QZbA7fPgJau7+glx4zuYlP/ji31+NpdxkAgFg1ZswYG0gLhKYgJrd5dMMNN4R4ZfFFJ9AvvfSS7d3lSyfITmZVIJxKDAXktPGsLLd4pyDtokWL7McDBw60wywc/fr1c3FlgDELFiywpe0pGT58uFmxYkWKE3i9aVNCpd4KsM+YMcOEQ/PmzW2gjqm2QPQhiBYntHOqwJFS6Rs2bOjKGrJkyeK3Z8jevXs9ATRllmmNSu/3pjcI353KrFmzBrX766yhSZMmtozCeQ6dgGjap7Ld1KuENyMAQLTbvHmz7bvj/d6ZEr1n+r6PKotBZZwFCxYM61pjnTLedQK9a9euJNfr51exYsWgH69169Y2s029k1QpEO9KlixpTpw4YY/BrrjiCvP888/bj3VRVh7gFk3X9R6Ip+oZf+cBOmcI9m9dCQXr1683Bw4cMKH222+/2Sw3tdFRkB9AdMlwNsE6n6pMUAeZ3mWICK+lS5d6poIpwBfpKa7qc7Jq1Sr7sXp1UFYAAIh2yozQiZSo7861117r9pIQhKefftoMGTLE/PXXX3FfSvv999/bTL7KlSvbz9UDjexHRIsNGzaYL7/80rZ8ad++vYkVWuvYsWPN77//bgPVAKInVhT0dE4gUpM2Q8W7T5rTNw4AgGhu0aCm9qIAjHcPUcQGZZHoYFw97eKdBjJ4t+nQtFMgWqjx/7PPPmtatmxpYon6KiqLWJmtao0DIHoEHURTWZy/5DVdp68hsSlgpt4DU6dO9QTPoimIpoEIAABEs+3bt9sSTlEGmm8LBEQ/NddXCwllw8dz0cfRo0dt39umTZvaIQxOGw39DgPRQqXZqZXER5sLL7zQTrhVJpoyPQHEcBBNDVLVW8qXmtkmQvNUpGzZsmVm5MiRZvLkybafSzQE0bx7wRBEAwBEO/UX7d27ty2Lq1OnjtvLQRppwIB6ralPa7xSvykNtVC2zNVXX+25nmw0uEmBXGfgWizTxNDXXnvNvPnmm6kORwAQxUE07ab5a8ionSg1EEVi857KuW/fPvuvE3RVSYobu0AE0QAAsUbHVOorpab3iE0KgKqxvsqy4tWoUaPs96nMu2rVqtmMH1E/v9OnT7u9PCQgDTR755137EU9CWPdU089Zf/G7rvvvmQnMAOI0umcjz/+uP1XAbQXXnghSTBEb5Iq4XMaiiJx+QbRFHTV74Wy0dwKsl5wwQUmb9689k1VPdG0M3XeebQDBAAA4aNjZmWjderUyWzatCnuKjZUsjlr1iw7QMEpP6tUqZLt6VerVi17vOUE1YBI0HnHmDFjbA8xVcQoI/Kuu+4ysUx/Q+o7qL8tvZZo2IC/hBYAURhEUz228+K0fPnyJP059PGVV15pnnzyyfCsEjFDwSrvIJpe5Js3b27cpr5oCqKp8a1GUXsH+wAAiAZ6n9KGk4IRiA8qc+zRo4f56KOPbGZMPFG/Jm1Uegcp2rZt6+nnB0SazldVQi3K4r3ttttMPChUqJANVutvTZObY2nKKJDQQTSnn0O7du3M+++/n+LITySurFmz2hOAEydOeMo5o4FKOtWvzSnpJIgGAIg248aNM6tWrbLTOJs0aRJzjbBxLv0fduzY0QwePNi88sortrVFPNCmuko5NTzhoosu8lxPAA1u+eeff8xXX33l+Vw9JeOp1VCzZs3MAw88YLp3724HzpQqVcrtJQEJK+AgmkPRbyA5yjxTNpp6EKhuf+7cuaZAgQKuv9D7TuisWLGiq+sBAEQPbfwoy14nYcm5/vrrk3y+YcMGW86mUpsyZcoEtTmjAMSff/5pM6Mdp06dsmvQ15RJ0bJlyzR+N4g2Xbt2tVlon332mS3HigdLly61v8NqeA6EmtrArF279pzhADVr1rTZj94DBFQqLWvWrDGHDh2yH6sXYTy2GerXr589t2rVqpX5+eefmdwMxEoQ7dixY/YNUz0Q9ALn++K2cePGUK4PMUgnEgqi6Xdj9OjRpnbt2lERRNO6lJHme6KjvgnsnAJA4lJZ2qJFi1K8jW8QTWVDs2fPth8rE+fll19OcnKXEp0EqW9PSs9FL6n4UaRIEZux9eKLL5qJEyemens1EL/nnnuCeg4FEh599FF7TBMJ6jelieu33HKL368rGKxzAgVCbr311oisCfFBgbC3337bTn31pfZB3q+zq1evthNivemYvkWLFnHZN0yl/nq/Us/Ba665xuTLl88kkhIlSph3332X4CFiL4jWoUMH8+OPP9o3eAUk4vEFCumjHXntUEaTyy67zI6I9jZ06FB7EJgpUyZ78gMASDzKml68eHG6T/oUhNNJTWq0wTRz5sxkv65yP5XqIL6olPPVV19NdWLlunXr7DAvlW4Fc6KoTDcFZzXRNVLHeio5Tm4TUuWrzrGgMoK8J6UDKZk6darfAFqgbr/99rhu26IpuMpqTWkjJh4pMK/XlaNHj5phw4YRg0BsBdGmTZtmpkyZEtCBIhLTddddZ/Lnz+8pU9FOZbQ2cNbJk16ET548ya4GACQglcTo4FyqV69ugwOBnsgoA83Jgvjpp58COjbSe4/ec0TTGr0DZno/KlmyJIMF4pDaSHj3a0qOSiR1W/1eKZsm0CDuyJEjzRNPPGGDddFAv8dOEE0TEhVwA1Kzc+dOM3/+fPuxM7TivPPOS9J72Vv58uWTvF7qNbls2bIm3um1IdDXh3jy+eefm3vvvddcccUVpmfPnm4vBwks6CBa7ty5TZ48ecKzGsQFvdkFehLidnba1q1b7cmT+qQVLVrU7SUBACJMJ1y7du2yw2d0oq+JboHQe4bK9JSBpr48ej/ZsmWLvS4l2ljq1auXLQfVMZVOBgCHejnVqVPHTvMM9CRZDf41fTya+q0pID1+/Hibeblw4UJbzuodDAH8mTBhgqdVkEqFU9uYULsW777HiG+adqx+pM8//7wpXrx40GXvQKgE/W6mkjj1dEhPmi0QLUE0h5pDAwASj3qsaOLZW2+9FXAAzTtzzLtXmsrpAqE2AgoyEECDPw899JDNbHSmiqdEAYf+/fubpk2bRlUwIUeOHDYg6GTKaeoskFops/M7r4yyG2+80e0lIQq98MILtq1U27Ztzbx589xeDhJU0EE0NfP77rvvbLme0s2vuuqqJBcgVhBEAwA4smTJkqb7qazTua+y0thkRHrdeeedNiCmbLTUqL+emvd369bNRJurr77a8/GCBQtcXQuinwJnlSpV8vQ1o80Kktu8GjJkiB2uoNfK9evXu70kJKCgyzn1ywrEA4JoAID0Ut+emjVr2qFLmoyoYIG/5u7qg6YJ5yrhBFKiZv0PPvignVCYWoakstAUeIjGYRTKRFO/KjUC/+OPP2yAOVu2bG4vC1FKpe6dO3e2k2ZTK4tHYtP7rsrFa9eubRo2bGh++eWXuB4mgTgIor300kvhWQkQYTqw066XygzUE0290Zj0AgCJQc2rVVapLPrkJgwGM1BHQTRReUndunXPeT/RBFA1RVbAo1GjRkk2cgBfCqK9/vrrZsSIEebRRx/1exsFGyZPnmw+/vjjqDx+0d+XMjXnzJljTp06Zf8G9LcCpEQDV4DUqEe7hh0q41X9TL///nsbXAMiIU0dPg8ePGiGDh1qnn32WTvdUJYsWUI2D2KOcxKj7AAF0wAA8U8n9JMmTbITDdVfRRlk6aHSuypVqpj69eubrl27+g1oKMim/lXKyHGmcwLJKViwoGnWrJkZMGCAp9G6r4EDB9rNwFatWplopZIrh6Z0Ar6c6chAWnqaTpw40Q4vUW9TfpcQtUE0NXwsVaqUTS9/5513bEBNlFKpoBoQS7wzAf766y9X1wIAiIylS5eaI0eO2I/V3D+9mWjSsWNHO4HQX0nJ5s2b7fROKVy4MNOgEfCAATVbV98zXyqN1IZ2+/btTfbs2U20KlSokOdYS5lzmoQLeP8e65xS/SSTCxYDKVFJpzbElOn9yiuvuL0cJIigg2iPP/64nYahN3XvJryqRw50KhUQLeiLBgCJR5MPHZEoL3NKPaVOnTpRWXqH6KM+Zyr/Vd8zX6NHj7Yb2cp8jGb6XdffmEquVJaaL18+t5eEKKJhddpgGDZsmJk6darby0GMatGihendu7cNoo0aNcrt5SABBN0T7bfffrO9F/wFI3bu3BmqdQERUbJkSdO6dWv7+xtNo+EBAOGhYxVNM3QaWSu7PtS+/fbbJIGzf/75x/6rpurqEQUEGoBSNpqarav3mfeAgQ8++MBuYKucKdpdf/319uKtZ8+e5t9//7UN5JXFmZbpuGrDoeBLjhw5zP333x+SjFJEzr59+8wPP/zg6Z/nXfoLBOuZZ56xkzo7dOhgMmbM6OpwCp1X0tsvvgUdRFPDvsOHD59zvQ5I2V1CrNGUtGuuucbtZQAAIkSN/x3KkAlHVph6rKlMyZdOEjNnzhzy50P80kbfc889Z26//fZzvtanTx8Tq/T3oSDaqlWrzM8//2zq1asX9GOoF5IqY5xNUWV5InZos0H9KUUTjZmuiPTQe/mgQYNsZuO9997r6loU0J8wYYLd6EB8CjqIdscdd5hXX33VjB071vMLq1/WHj162AaoAAAA0UgN/RcsWODJfKhZs2bYpj8ry82bPm/QoEFYng/xS/3OVq9ebfbu3ZvkemVuxXJvPW28O71o9TcZbBBNgWrvQQWatksQLXZs27bNNoN3fsc1lAUIRfBq+vTpNiPNrSEDel5tfDRt2tSWKKdlgwBxGER79913zV133WUPBlWeoDcslUZod7VXr17hWSUAAEA6/f7773Yas1x11VU22BUOt9xyi70AoZA3b157iSc6yVRD+S1bttiAivrSevepDWTQmTcF5BRojLefUzxSkEED6Ry33nqrLXUHQkGlnKVLl3Z1DepZqcQjXb7//ntKleNQ0EE0jdLWL4PKIfQGdvToUXsgetNNN4VnhUCYKRisiVE6gFP9uia1AQDiT6QHCgBInjJBFURzstGUuREo7yw0KVOmjDlx4kTI14jQW7lypVmzZo39WEFP3355QKxT+6tvvvnGZp8rSKzef1WqVHF7WXAziOY9MUgXINYpgOZMvrrxxhsJogFAnDax1uu9aJBM8eLF3V4SkNA0ZGPcuHHmzJkzdnDZnXfeac4777yABgqol5pzsvrCCy+YPHnyRGDFSC/9Xyu44GjcuLEtrQfijbIrNRDm5ptvtpnpGjZUrlw5t5eFEEn9ncqPWbNmmdtuu81OBNJFH8+cOTNUawIiyrt8QNloAID4o6bV6umqXWEd1IZjoACAwKmcukKFCp7AmHq/BUK9tBSMkbp16xJAiyHKIPz777/tx+rpp2omIF7lzJnTTJs2zW7cqWpPvdqQoEG0AQMG2NREjZN+5JFH7EW/IJo+8dFHH4VnlUAY6ffX6YvjvLEDAOIzkKYph+EaKAAgON5/i87Qj9QUKVLElkYpg+nqq68O4+oQaupVVb16dbuJofJdNjMQ7xTkVyssnW+q4kkDGRH7MpwNcnRFoUKFzDPPPGO6deuW5HoF0Hr37h31mTyHDx+2fd2046VfZkDef/99T3+GN998k98NAACAMNOUzWeffdYcP37clma+/fbbdsJeoD1ts2bN6vlc2Wk6litZsiQlglFu9+7d50wwBuKZYiTqxarXprlz55oCBQq4vSSkI1YUdCbawYMH/Y5oV62vngyI9ZJOstEAIL44pV8AoosCZjqHaNKkiXnppZcCDqCJdwBNPdV69uxpPvzwQ7NixYowrRahQgANiXiuqZZY2jBQaaf6tCJ2BR1E06hW74aQjokTJ9reaEAsoi8aAMQnTf97/vnnbYPfAwcOuL0cAD4URFOfwly5cqWribezma+eaYgup0+fdnsJgOuKFStm+8grE7N+/fokIMWwgHKdP/jgA8/HmirRq1cvM2fOHFOrVi1Pk8j58+ebJ554InwrBSIURFuyZIltdJs/f35X1wQA+D+arOk05a1cubLJly+f52va0dVrtz8rV660WfRTp061J+lMFgdi08aNG20gvFKlSudkrJUpU8aW3qgUZ/ny5WbGjBmeflvKWKtatWqSzLVoCCotXrzYcxKtElQ12necOHHC/PTTT/Z70NR476/Fml27dtkMQQ11UQ+7jBkzur0kwDV6rVIgTUNRGjVqZL777juTPXt2t5eFcATR3nvvvSSf586d2x6U6uLQgemwYcPsbi8QawoWLGgPVNQiUCdqKukkiAYA0UGvye+++66nLFO9RLyDaHv27PGbJe8tS5Ysplq1amFfK4DwUHPupUuX2mDYk08+aY/dHArM6O979uzZ5tSpU2bChAlJ7rt27VrTvn17Ey0U5Pv22289n6uc1TeI5rymqYeSzq9itQRS/xf79+83n3/+uTl58qS54YYb3F4S4CptBEyfPt0OGtCmoPdrWaypX7++LaVPNAEF0RRUAOKZdjTLly9v/vzzT7eXAgDw8eOPP6a7r9n1119vA2kAopPaaWhCp0qwH3300SSTG48ePeo5RtMxm7+AkrJM1bBbQTRfylRt3ry5Zxq7m/RapiyzQOn7mTdvnp1mGWsUvFTgU9Ssu3bt2m4vCYgKNWrUsBsDH3/8cUz3bc3ntaGZSBhdA/x/HTt2tG/22iVTzbrj2LFj9sUtR44crq4PABKRJvA5PY40ve++++4zl19++Tkl+XoNT6lfkkqiAESvsWPHmnXr1tmPFUjzzszS4ACnr5ZOPv2VBCpD9YUXXjB//fWX57rff//dbNu2zd7HOyjnJk0QVYm56HVJmVmXXnrpOa9ZypwbOXKk/b7VOkd9qWNp6qiOnb/++mvP51q/XsMB/I/Km3VB7An6lVjlbuPGjTM//PCDbYrnGzkdP358KNcHRDwbzbF582YzePBge6Cjprd33nmnq+sDgESkANq///5rP9aJ8FVXXXXObbTJUaVKFRdWByBUdDLpBNGUkeYdRNPn3rdLKSvCOzNCPW4VeIqWAJooIOZQXyR/r12ZM2e25al//PGHzaJTJt6yZcv8vv5FK/2fKYAphQoVMjVr1nR7SQDgznROpVdrF1glnkqJVmqu9wWIF2pQ6+wUeu9qAgAiQxt3Ks/yLskEEJ/UG8gZGLBo0SLz33//eXoibt261X6sLFTfrK2U6PGiKYCmzFoFxpxss4oVK6Z4e+9BKCrpjBXq6TZp0iTP582aNTPnnRf0aScAxEcm2qeffmqzzRo2bBieFQFRQgM01LxWBzzq0wEAiCxN49yxY4f9uESJEkkmKQOILzrmUiBNpZtqpbFixQr7uXfmVqxnM6m81AkOKtPMd8qor1KlSpm8efOavXv3mtWrV9t/9Xm00/RBZ/KomqiXLl3a7SUBQMgEvSWgbLPixYuHbgVAlNLOpdLPRQcCR44ccXtJAJBQyEIDEot3kEzlgOoHpqCaqA9a9erV0/zYCsirkbcyXN3iHRAMpBeSsre8m/H//PPPJtodOHDA/pyd9cfiQAQACGkQ7eWXXzavvPKKzc4B4p131gPZaAAQOdq4cMqe1D5CGSkA4luZMmU87WE0jXPx4sWejCb1N0vrdM0vvvjCvPbaa+abb77x9OmKNPV2dDZkNQShSJEiAd2vVq1anlLIX375xTNgIVpNnTrVk22nnm/+JqkCQEKVc2o89JdffmlfENXw0zcNWc0vgXjhZKI5QTQd3AEAwk8ny4899pjNRlOj8NTKngDEPgWLlG2mckAFi0aMGBGSUk4FrJyeYhpW4jvhNxI0mfLFF1+0g6uUjBBorzYFFdU7TZl0ykrTz8XfdNJooSmc+t60CUL7HwDxKOgg2v333293he69916TP3/+qGrWCYQzE43hAgAQOTq+UPsIWkgAiUVljgqiOX3S6tSpY/ujKRMtrTQBc8yYMebUqVN2aEGTJk1cCUTpda1YsWJB309D3fSziIXzLk1LbtWqlf0Za80AYBI9iDZlyhTz3XffJZkWA8SrggUL2gMW9c+gnBMAACC8NH2zcOHCtuxSga569erZ7Kb00CRMBeGUHXX48GGzZs0aU65cORMrtP5YQwANQLwKOoimN7WcOXOGZzVAlMmcObPNuNy5c6fNRIv2FHq4QxOzRo4caY4ePWo/f/vttzl4BPzYs2ePGTBggJ0wl5ru3bvbyXQAEs9tt91mNzDLly8fsuOuGjVqePosfvTRR54+Y9osVblk+/bt/T7XrFmzzJw5c2wgL62DDdQLTRlakTB58mQ7lKFZs2YR6yWpHmgnT5402bNnj8jzAUBMDRZ49913zdNPP23r+YFE4DR+vf3222MijR6RN3HiRNv4WEHWaG/4C7jpp59+Mrt27fL8raR0cXOCHgB3KahVqVKlkG5cKiDnZHTp9UWvM+q1qPLRm266yRNU86b3dg0j2Ldvnxk9erSnYX6wAwXUC+2tt96ygwHSS73RFCg7c+bMOV/bvXu3beyv9Sr4F8mpoy+99JKZPXu2LZkFgHgWdCaaeqEdP37clChRwr4R+Tb63b9/fyjXB7ju1ltvNceOHbONXXWApd1EnQiqzEC9em6++Wa3lwgXKaNmy5Yt9mNln+XNm5dgK5AM9cjRiez7779vy+UzZcqUYhNuAAgVnbO0adPGfP/99zZrynmd0fTL5PqUaQiBE6zSMIDly5ebq666KqjnVfabAmk6Vti4caN9vrRSQE/rF61Zr6fefv75Z8/HCkJGgoJmavWj88Nx48aZK664wpXBDQAQtUG0fv36hWclQJTSJNquXbt6PtfOpXYARQdFBNESmwatOPS70KBBg3M2FvLkyePCyoDoowBz6dKlbUknAESaAkuBBpeUreYdlHIyroINoqm0MhQTRsU72Dd//vwkQTQdn2p9ogw+ZdhFgr4/J4lC6yGABiDepWk6J5DIlJGmvhbKSFOfNB1kkXmUuLyDaNWqVUtycKseKn///bfp3bu3/b0BAACxYdOmTbb83NvKlSttiWeg7+kKLmmIgeTLl89W8qS3zFW9qTUcYdmyZfZfp1e1suT0uShQGIkebArcTZ8+3fN5w4YNw/6cABBzPdG2bt2a4gWIdwqYFSpUyH6sRvIHDx50e0lwiQ6uFUh1eueplNP7wFkTXRVk/f33311cJeA+p3QKAKKR2nb8+OOPZtSoUZ7rfvvttyRVCU75pzbHAqVyUKe/o7LQ0rvpqgwzpxxUZaZO5pmzeee45ppr7L96bvVHCxd9f87jly1bNtmyWABI6Ey0okWLpvgGQFNtJAJNqV21apX9WEGU3Llzu70kuJyFVrVq1SRfU7nHtGnTPLerW7duxNcHRIuhQ4fafjnXX3+9/VthyjGAaKISc2WeiYYMXHrppaZp06a2v5eCaZrMqYb+yvDy7QedHAWwvINc6S3ldNSuXdv2IHMCZ1qvNnSVJSdqIVGmTBmzaNEi2z9NE+aVER/qyZlkoQFIVEEH0XwzKjSlRtf17dvX9OrVK5RrA6KWk4kmGjCg9HokdhDNt0eKDsALFChgD143bNhgD3Bz5crlwioBdylLYcWKFfaEUn8H3mXPABANqlev7gmiKfClAJqCZQr6O5tkel8Phh5P0zKlVKlS5uKLLw7JWlUWqt6SKhPds2ePWbdunT3OcDLeFGTTICwNMdAxqmgqqIJtoaTgop5ftJ70lqoCQNyWc1555ZVJLjoY7tixo3nnnXfMBx98EJ5VAlGYieZwDlCQWDRUQgfEyqjRlFbf4QHK2PXOTluyZIkLqwTcp2nGzsmdSox0cgcA0UTnM06GrEoUQ1FZ452FFuom/065pvMa6wxA0LGH81x16tRJchtnymg4stAaNWoUsscGgGgXsiNZ7UB49w4A4pl2AdUXQ5yeWEgs+v/X1Na33nrLtG7d2u9tvLPTvLPWgEShbHXn5E4nqMqQAIBoc+GFF3qqCtSc32nZkVIPtZQCbeoD6bzv63ihcuXKIV2vEhmc8kxt0jnTMcuVK+fZ1MufP78t6xRljKX2PQVD35uTZVeyZElb9goAiSLoIJreWLwvmlCzevVq8/zzz9sXUSARKJPCSetXqZJ6/SAxZcuWzRQsWNDv13S983uisg7nIBdIFH/88YcdwCJVqlRhSi2AqOWdLaYeaZp26Rso27JlixkyZIh59tlnzZ9//pnsY+m4UOdFOl7Ua1+WLFlCulaVmnqvt0GDBqZx48bmxhtvTHI772w0TQwPFQ0RUHlo5syZ6YUGIOEE3RNNPX18BwuoTEPlbaNHjw7l2oCopt95p3+GstHU7wLxSSUQKu/Yu3ev5zpNoCpfvnyq91VJpzPJ64svvjDdunVL8rhTp04NaA1qSKwMyFDQDrkyh7VzrJ3qWHDgwAHbV0u7+coYQGyYO3eu5+PrrrvO1bUAQEr0nq7sLmWZycCBA21Dfu9+pkeOHPH0h1a5pjLC/NF9OnfubG+vjNxwUEmn+q7qX2XR+RvYouuVmaZNPA0e0HGM9yTxtMqRI4ftG6fgXdasWdP9eAAQ10G02bNnJwmiaYdFJ3Y6GcuUKeiHA2KWUuYVQNaQAWf0OeKTGvJ+/vnnSa7Tbm+gQbRvv/3Wk7XmK9Agmhr2hiqIpmCegoI5c+Y0L7/8csh3yENNwcYPP/zQniwsWLDAPP744ylOiUZ00OaCml07WZmU+wCIZgpCafqm3vNFmeS+A4GUgaX3TlXjKBNNmbYpbewo2BQuGl700EMPpXgbnadde+21ZtKkSfaYVRsbCn6Fir/jGgCId0GXc9atW9emBjsX7Syr3p4AGhKNDrTuuece+zfA1MX45t0cOFgKsCrgKtFQyqYSE2fIgU4CVG4X7dTHRQE0UVBm69atbi8JAVAja8f1119P4BNA1Ktfv74NDCmg1qJFi3O+rutr1KhhP1ap56JFi0y0U6aac56mHpXKRgcApF3Qka833njDlv+0b98+yfXDhg2zTSt79OiRjuUAQHQ5ePCgHRPvBMSaN29uPw5mVH2nTp3M5s2b7e61NwUVvMs7A50Imx4KoJ06dcrzuXbcQz01LNTmzZuX5POlS5eaIkWKuLYepO6ff/6x2Y5OU23npBMAopne51988UX7Puk7dduh98yZM2d6NtmUYOBQtpc2ftTiI1oSDJQNp0FHek3WRpoCf2kd8jJq1CibFa8NZForAEhUQb+6f/zxx7YUyJfKmlq2bEkQDUBcUe8THRRLtWrVPFllwTYA9jd4RUG0tDxeevhOUV63bp3dAAlVqWioaXiNmjs7Hn30UYbYxACnqbb62CmARs8cALHCd8PLl8o8tbG1bds2mxmtvqfOECF93r9/fxtgUsN97wCbm1Q9pOCZerg5aw3Wjh07PJn5OpZ44YUXyDAGkJCCDqKppMbfJDqdgOnFFUg06oeh3j/6u4iGcj2EltNAWLST63ZvsDVr1tg+KLlz507T/fV7qgN+p3Gy6KD49ttvN9FImXJat6iBMQM8YoMyNbt27WqnF3OSBSDeKBtNQTTnPdTpM+YEmXRsGC2ZaFK0aFHTq1evdB2nqi+2Q33WeG0HkKiC7ommnZf58+efc72uS+vOBhCr1KD16aefNh988IHNuEB8URaU0xhdZez+NhAiRcGz5557zjbYd5oeB0sNhpUxrLJ8NefX585BvxOoiiZak/N+o4N19XVB7AXTkiuJAoBYVb16dc80TGVlqT+apnA6PdKUga7BQtFC76HpCaBpyqgG+4iGEdWqVSuEqwOA2BL0FknHjh1tOY3eKOrVq2evmzVrlg0kPPHEE+FYIxC1FFhxODuSiB9OLzQnC83NXVf1adFBrKivya233prm9Wh3XJMSK1asaB8jWg+GV69ebTOZnIlovn3o1LMmmnb6AQCJQeWaFSpUsD06teGm9ys17HeyvFU2GU9l7BoU4/RT1YZWPH1vABCsoM8+nnrqKXtSozINZ7qLdiTUC+3ZZ58NegFALPNu9q6STsSXKlWq2KwtTbAsXbq0q2tR+aYCX+phtnv3brNlyxZbnpEeDz74YFSXY+zdu9dkzpzZvteodES0gaMgojLULrvsMtO6dWu3lwkvmviq4LMCtE6WBgDEa0mngmiFChWyvVOdTC3na9FKwTCn32sgQ1/0vqvKC9ExQ7T0eQOAmCnn1IvnW2+9ZRtRqwRIbx779++3k2zS6qOPPrIngwrG1axZ0zPRy5/x48fb5t65cuUy2bNnN5UrVzaffvppmp8bSA+NQXdKlRREi8aSOKS/wfD111+fJOvQLd4Hu74DAlKzadMm26PFWzQH0EQ/dwUxW7VqZYMyopKZcePG2WmnKps5ceKE28uEFwU3Bw8ebBtOqwQZAOKVhqopgUCtFjQx+s8//7TXq2yyTJkyJhrpPVOvz8OHDzcTJ06076mp0XutNkiczcVgppMDQDwKOojmncasfgBKZdb4+rQaM2aM7c3z0ksvmSVLltj05/r169tMC38UsOjZs6ftCbRs2TLTrl07e/nuu+/SvAYgFNlo//77r82cAcJFB69O+eLixYsDOvgVBXeHDh1qnnnmGRvgiKVgr0pGlIXmZDVps0XvPc7fnNN/Bu7T76NKfkTlTXnz5nV7SQAQNno/do4BtbHlvLcqIcDpORpt9B7qrPnAgQNJpl/7o2w1te1x3HjjjWFfIwBEu6Bf4VXrrx2M2rVr29Ki4sWLJ7kEq2/fvrbPmgJh5cqVM4MGDbLZPcOGDfN7e6UQN2nSxPbHKVGihHnkkUdMpUqVzLx584J+biAUlMbvoC9a/NCBY7TRa6N2vkW7wqtWrbJlFqnRcAQdLOsAX7f3PbjXddrEUIZXejnP4X3xDdqlN4h33XXXeT52gjaIPKeRtnPRxtbBgwft17TBRrYCgPS8B2tjUsdVThZUNHOmcjpBtGhWp04dz8c//vhjiu/ZyijWRG8pVqyYvQBAogu6J1qHDh3sC+59991nJ9WlpxxIfW6UTeHdS00ndzfddFNA0+f0Bqtxy3qBV4mpP8pU0MURC2/EiO2+aNE0jQlpo9eJN99802bG6mA4vb3HQl3SqTJ6GTBggC0bUcljSrxLP50sLt+S+rVr19qPtUGS1knLCqKMGDHinBJLZcBdfvnlns+VPaagnXqy+dutV4sAZaAl17hYf3N6vK1bt9oTLPWHUylNovr+++/NjBkzzO23325LYCNB/XG+/vrrZIO4kVoHgPik/ss7d+70ZLZqo14bSdFI1TPHjx/3DAFyc5J3IJSIkC9fPtuaR+dQSkjwdv/993sCgdqAU/aa3tfJQgOANAbRpk2bZqZMmWIns6SXdpi0k+3ba0ifa8pNcvRmqobSCo6pxEcnkjfffLPf2+rk8pVXXkn3WoFQZ6KpWb0CFzt27DDdu3eP2tT/RKT/G2XUaMNAB4/RFERTho+CS//880/ADYQVsBI16Vfmri8FC50gmjYwmjVrFvS6tKnxzTffpNqjTDvcuq0CbitXrrTfjy8FZ/Q19b+88847bf9LXyrx/OKLL+zHykRO5CCaJsdqU0qbSvqZReJEU691yZUTq4xTJ2kAkJ4sNN+gWrQG0fS+p2MFqVevnol2Ot7URofea1Oj6d1qJaHNOB0rAADSUM6pCXFOI3W35MiRw57k6gW9V69etqfanDlz/N5WWW4KujkXyu0QavqbcE7yg5nQqQEa+r3VLmByPQDhDifo5AQoosn5559v2rdvb8vfS5YsmWoZ/YoVKzw75DoAdg70vSk7zem1pt/LQHutedNExl27dnmGMWhtzsW7b6aCdU4fM39l+HqdVqadNknUq8XfekXBIudrei8INKgYj2bOnGk3t/Q64l1SFE6aiqr+pMqE9P6/VlBU7RnYFACQVmodow0g3wzxtLw3RYI29vWerJ7OziTpaKeSTl28X7+di97Dvem9Vm0UmLgMAGnMRHvttdfsJM6RI0eme0dIu9V6QXZOvBz6vECBAsneTwfn6scmms6pvkDKOPM3clknb+kZfACkRiXNykZbt26dDfB670j6y+RUeYLK0ZRK71BZWkq/84icI0eO2P9L0f+Rd6ZhtFBfNKc3mjeV1ul3Sf0iAy3ldAbFaPqlRt7r+1fgzV/GWkp+/vlnz8fqW5lcTxgdoOv9QzTJTBl/mrbsUCac049FO+DJHbQ705yVLagsLAWPbrjhBpOIdCKkn4PoX70XRiKIpZKl1EqJASBYKiH0l52mTRa3N/L90ftU06ZNTSzRxlmLFi3cXgYAxKSgj7LfffddOwlTJZc66VKWhvclGCotUv8o76kvOnnS5zp5CpTu4933DIg09Y947733bGZGcgE0UWaNghrjx49PUn6mwAeigwJJzlABvaalp+9jJGlUvXqP9evXz+7ii7KznMlbCpSlVGLn/ZobSE9Kb3oe9bcUlZqq9COlkw31XXNeu72fS5/Pnz/f83lqbQN8BwxE4zCIcHKCjQrAlylTxn6sHjfaWAKAWKRss+T6FzuDSwAAiKlMNPWnCSWVYioAodIcNcx2TgBVDiJt2rSxadLObrf+1W2VaaHA2dSpU82nn35qBg4cGNJ1AcHwzqRJ6cBw+/btngwnZeQ4CKJFVxAtWks5U6LSF6ekUd+DSkpU9u40fteGRUqlGAqwqTRPO/0KvCkjTZmVgVAATdlgotdnbZCkREG06dOn26CXMthUAqPMKZU2q++Nsx5lK6dEAxCUlaxSUm3sKAs0uWEE8Ujvl/oZqbeNLk4vUWWj+ctUDAWV42o6m8qKASDU9B6U3IaIWhPovSa19xgAAKIqiPbSSy+FdAFKJdbOuUpEVeam8kydXDnDBhRc8C5LUYCta9eutveUTpa0+/7ZZ5+RkoyopzJlJ6Chck4FKNRPTWUL+n1WVgl9hNylwJHTYD9aSzmTo1JNJ6tX2Y4KogVSyulQgE3lkZryqN9F9UYLdBKXflbqB6MMqECGzlx88cU2SKbhAQqaKfij+3v3SAu0r4xe+9WTMJBAdjzZvHmzWb9+vb3o9aNHjx62zEmTTVWOq/dV75LxUNBjf/DBB/bnfcsttzCpDUDIpZZtpq9rAiYAADETRAuHbt262Ys/vgMDXn/9dXsBYo33UAsF0eTyyy+3QTRl0Oik13dSLSJLmVvODrhKEmOllNP5ndKJhZrLK7CiEw2V3GvnXhdlD6VGJZ0KoonKLDVlLJCfQenSpe3Ft79ZShQkUxBNVMKpgKUGCoiaGgfak02Zyolo7ty5SXqiKQiq8laV9ep3WF9Py5TVlCjIqQCrgs2JPMgBQHiowsQZhONQ1pmT6Sw6ZtIGQSy9PwMA4ktAaS/a3XZGTTvTOZO7AIlqwYIFZujQoeaVV16xQbFAg2gOSjrdF6ulnKITCifbTEEUlViq0b76pGlKcSAnHAriOsG2v//+22YHByOYbDAF+JwJYAqeORlwqQ0UgDFHjx71TDhVRrbz/64yWWfKqoKg3iee6aUsWidTUBmzsTKBDkDsZqHpfcB3o0SvRb6BNgAAoi4TTQ3Tnd446sEC4FwbNmwwS5YssR+r95n3hMTkgmjOv04QLbWSO4SPDswVOHLKDb3/b2KF+pFNmTLFfqxSTqfcLpg+Yco+U3ng1VdfHdbMSJ0cKVimQTUKns2ePdvztUBKQpPrC6d+bldeeWVcl0arj5y+V9HP0OkPpPdp/Q5oUqlOMvU7kNafpS+9til452RpJlr5LIDw0uaPbxBNfTqzZctmLrjggiQDxHQ7lZUDABC1QTQ1/vf3MYD/490/SwEz7yCaggROEE0nn05Qmky06KFG6b169TLr1q2zmYSxWCqioJd+p/S7pIv68AUbCNMAAl0CobJRlfWpn1laMscU4FEQTWWo+vnrJEmN8lMbKOCPerhp6q2munXu3DngctBYo9cSTSJ1aKCAN5V2Koim3wMn0y8UNKzA+zkAILWgmN5LnQzj1Oj13+kb61D1i96Lddyk9zPv4QMKsEXb+7Q2NBi6AgDxLyp6ogHxwDtzSZk83tQ83Snx9L6dTnKVfaagQfHixU2sUxaQ05hf1DxeAZZoOaBX7y3vA3HvKY9OCaGGlcQyZSI5AVllIt12223pejz12HKynnypp9mOHTvsycyTTz5pM/iCod/7F154wRQoUMCeDOlkK61lOsq2UwBNFGRyK4i2adMmWxbs9NbT9xaqbDDR0ABngqn+tnwbbBcpUsQ899xztgQqvSeYeh71JVVZqAYZOJsFvlm2AOBN7xmampyeknJln2XJksV+7BtE03uF85oUbfSan5aNIABA7CCIBoSIc9Kqk2fv0s3kSjkd7dq1M/FAB7iDBg1KMpr+hx9+sJN3o2GSljKVvvjiC79f01RgBdHigbLIlJElU6dONfXr10/XzriCKN5lNP7o8ZUxkBYFCxb0fKwSzAsvvDBNj1O+fHnPdEpn6mewQb30UsN9Ta/0/nlVqFAhpEE074ww3yw0R6imyqpkypn46p2FFm3ZHwCiiwYlpbcno5OF5rzHqHzz2LFjJtqpl6g2lshIA4D4Fb9NYwAX0vidYJGyc06fPp0kcKZJeTVq1DClSpUy8UhlgyqHbNy4cZLd4tWrV5tooEb7iUAnHspGE/2+ReJA/pZbbnG9B5l3s3sFctU3LNLUzD+1gGN6aPKqM9FUAUIF6CJJE/Ho2wggJXrf9+1tlpbXcwWivEV6UyQ90vv9AwDiIBNt2bJl9mDd7ZMkINopWKaMLJUyKJDmZITo5NNp8h7PVHKhMjod/I4aNcozcCG5jJlIUWBjzZo19mOtrUOHDkm+Hm8Nitu2bWsaNWpkf+/S6+GHH06xp436+0VDpqFoGMLkyZPtehVEa9iwYcSmfOo5586d6/lcfdn0e6Wm2KGiclUFq/Uac91116X6nqxgokqqdD8NAwgke0Ql5iqjcsqcn3jiCfuxnkvZts4QAwDwRwNIvDcRJZjXYW386HXOdwNIr016rzlw4EDAfdYiRevxzsJXEE0lnWTtAkACB9F08K2AgN681LdJfXZiaUcIiGQQbdGiRZ6+aIGWVekATCfGOvBSH7FYpvI8vVZ8+eWXtkmwgmhuW7VqladhccWKFeO+p5MCHqGarFmsWDETS0Fcbfho40eNp9U/LFK90fRcKiV1epX5Pq8CWfPmzbP96jp16pSmk6srrrjClkcrIJzaa4tOYt955x2zZcsWe/Kp3/tMmVJ+y1fgWxNqVdp8xx132D5z8f63AiC0FOTypo2EUPV81bFFtGzaeFOZqfphem/caehOKDdRAADR47xAT0ycNwc18oy2HSAgWnif2PoOF0jJq6++al577TUzdOjQJLuZsUq7zk7wRYEFJ7jgFmXjOOJ1aiP+x7v/mAZJRIp3Fpq/zMuPP/7YZskpwJeehtgKvmn4RWq94/Q36PSpUwDvjz/+SPH26tuogLdO/BR0pp8PgGBps0q9Ib2ltV9mLFGwzPc1k5JOAEjwIJp6OamZsE6KdQCvfjvaVfJ3ARKZdxDNGSagBuc6OU2pV5Kzs6oTWJVUxeJUzrFjx5olS5Z4pit6Z7B4B7Hc0KRJEzsF8s477zSlS5d2dS0IL2WBaeNH/vzzz4icyOhv1ulVpuEG/nqVOf3anGENkaD3bX8DCfzx/jrDAwCkhTKAvel1RJmw8U7fp2+wUO89JB0AQAKXcw4ePNg0bdrUrF+/3vbH6dixo+2DAyAp/V3Uq1fP9sIoUqSIvW7BggU2A0UHWSrj8pcJdfnll9sTfif4Fo3lCilZunSp7UGl4MCjjz5qhyd4B9EURHSa3btBP3uVmXpPgkR8UgaWyhGnTZtmszrV7P/WW28N63OqvNLJIE2uV5kzNVVlPwo26z3Vt3F2clTqrQyPYKdu6u9Qv/Nqx6C/weRKzLUmtWmQLFmy2IEUABAMvQb6lnLqNS5SfSndps0bDX9xKICmrLxAX+cBAHEWRJMGDRp4Jtw98sgjBNGAZNx1111JPncy0nSAmVyfKgXRHOqZpBPuWOL0PdPBctGiRe3HylxVMKFAgQKezCAgEmrXrm1LORUMisTfkrLMFLD66aef7HP7o4b8KjWdMWOG7Vem9WnwQSCmTp1qg1wKTGtoRKA9SRU8VmnpmDFjPNlmrVu3Pud2CoA7PQM1nEGBNAAIhjLpfTPuE+m9X6/xGiajTQmHgooE0QAg/gQ9bnP48OGeAJp2tYPp+wQkIieIpol3yU1L9A2ixRLttCpTxvk+nOl9akrep08f8/zzz5v69eu7vEokEgWZevfubbO9IpXVqedR64OUNpiUpeaUSSrgpim+qVE/M2Wuyc6dO4MujapZs6YnKLZw4UJPuXVyU0W9S0ABIFC+pfPqERZvk69T4xs01KRSZ4MCAJDAQTQdcKsJunZWVK6mi9401BSd2n/AnHMA5TTVVxmVvzIv0d+Qc3KsoFssDRfw7nfmO8lPgTQ3qbTijTfesOW0KmlD4kjub83t4J5Tzq3eQak1+xdlrClzTZTJFmzDfwXQlF0mOplTeas3lZGrb6PTTy5UU10BJA4d//sG0dQjLNF6K+rcyPe9hwEDAJDA5ZyOnj17mk8++cS8+eabnilo8+bNMy+//LI5ceKE6dWrVzjWCcQUZXtoAp/Knx2FCxdO8T5OXzTdVxmevrfX9U6gWlltoZiep7/ZQLJhVKbpGxBTyYKCfeoHlVwQLZQUSNDBaTAH5ZqEqKCkLvqZ0RMtccuMnEBUSnwnXqb296G/SQW/gyl/rFu3ru0hKNOnTz+nV6D3WvX3pYw10e+9MtnSQiWdzjADlXTecMMNnhO9mTNnem5HFhoAf7QZqMB/cht8Ojbx3UhPpFJOh15X9Z7gHTjbu3evZ2KpM2hBA2gSLcAIAAkdRBs5cqQZOnSoueOOOzzXaWf9sssuM127diWIhoSng0xNgtTJsLdAg2ii7KkBAwYk+Xq/fv085dM6aX/wwQdNmTJl0rXWr7/+2ma6pKZKlSp2oIg3lcv5NhFOaUKvfh5pzUxTFlnfvn1tkOOpp56y4+QDDaI5/A10QPzTid+UKVPsZk9KVIb51ltvJbnuyy+/9DTcT45+p2+55RYbgAokmObd7P/vv/+22arewbshQ4aY1atXn3M/TfwMtBeaL/Ul1GuFHldr1Qmxhp8oCKgyVA0N0ufly5dP0+MDiO/XUL1WBUNlnE5rh0SjDDzvIJo2RbzL6LUBqWCb7zRPAEDsCLreRQff/k7cdZ1TtgYkMu0uqqm+L3/XefPO4ipZsmSKt1WGjDJKookC6f76QU2cONFmqj777LMBZQP5M2HCBHvgqd5r3v2b/l97dwIlaVneC/ydfe+ZYWaYFRgYBBURDVvEeDWCoHCjElRiTED0GuN1gUxiEKOM0eSghBguiuuNGO9VQaIkarxGJWBMBEGRRFFZHBiGYRZm3/e+5/k81an6urqrqrv2+v3OKYaqruWr6upa/t/zPs9wIpwoDDyIoMAytd788heBdqUAbTQiHP72t79d9fLReH2IarQQYVq++m0oo60SK9xmLCuPwCxEGH3iiScOXH87LoEFWisqqWrVywFRvK5WChDjMe2kth0AjLIS7ZRTTkkf/ehH0w033FByepwWPwN+NaEzlmoV9j5GFVRUgwwnguio8IweYxFIlQvZYnlEVJPEErPCwILRWLRoUVbhUkm5Krr48h1BVYgKnLPPPrvsZSNcL4x9j20uTO+sxU9+8pOB///5z38+MC14OA888MDAh1RVaL0p+tP83u/9XtaYv1LPznJVkhE45StK82JZdSyXrKXqIlohRKVC4e8nH7aPHz9+0N/aM57xjDQaJ598crrwwgsHVXHGY/SSl7xkIGQDKIjXv0qvgeVeS2sdgNJNYkdJfN4bbkhUTDGNnaGt7hsLwMiM6a9xV0hUv1xwwQXZ0rPnPe952WnRqDi+HH/jG98Ycc+WZolJZ/GlISoUevlNns4Vyxpj+VW49tprq65kCT/+8Y+zL+8xECT+hvNf1hshKsduvvnm7P9jeuFQYdtQYnvf/e53l4QW1113XcWecLEsLu5vWL58eTr++ONHtP0A0ItiGWfxKpP4zDDcxOP4eXwmUdX6q6AsdqQWvmbFzsTiHpuxPF+fVoDOzIpqfpeLJR8PPfRQtkc7vtzG4bd/+7ez5uLtHqBBN4jwq2C4PZ3lRP+zW2+9NQuh4sWhGYrDq0L4V4viwQWFCYMPP/zwsJeJ8/zsZz8b6M0yXK82AKC6iZvRFH+oQ3zhEKD9SgwzKn688ktc47GtVCENQHsaP9IlYAYIQGsUL62MEO2Zz3xmVZeLvaGF0C2CpfhQ1wyxrCGWLMSSkOhRFttRy1Sq/PTPCO2jkm44EfTHXuDCMjYf6gGgerGjLR/y9HKvs9GKdhxPPfXUwPHoERtL+q2KAeg8vllCB1ei1dIXrbgHU1xHs8arR4BVqASL2y/0R6vWqlWrsn9j+eY73vGOrGdUpVDMVE4AGLn89O1YptmrEzfrVZmW70mZf4wB6AyNb4gE1FVMmYwPY1FpVctyzuLzFgdxzRAVZNHoP0Q1Wi2TMqMf2mOPPZbtwa3UBy1EpVsMHyj0Zyk3TRgAKC+a3hcGIxWoQqtPNVrx4xo7FqNPWjP60wJQP161ocNEFVZM04uJmLWEYcUhWrlpm83qixYh2llnnVX1ZceNG5eFcHGoRlTYXXXVVVmQtmnTpuxxAgCqk6+QivfhGTNmtGx7ukU0q167du3AsIH4N5bNxpABADqHEA060Pnnn1/zZVpZiRY9zGJPa+xxjRBtpOID5/r167OqtvjgGf3RyokebL/2a782ii0GgN4z1EABvUVHL8LI6IFWPNgpAkshGkAPhGjxRfjOO+/Mvgz/7u/+brZ3KsZgxxtD9EwA2k+hf1r05Gj2B7ZYhhnVb48++mhWHRbLGfK9Qar1sY99LG3cuDH7QP+yl70sC8wAoF1E0/h4nytUHDUzpIn3xNEEXjt27Mi2v5ilnPVd0lkcosXS2QjS4nfXruL5FJ/ZBKndI77LxyT7aA/j9wpNCNGiyfdLX/rSrKolejLFsrII0T70oQ9lxz/xiU+MYDOARoq9ytu3b2/6UIFiL3/5y7M36qhKi+bE8SH95ptvHvL8seRh5cqV6aKLLsqGA8ybNy/b7mc961lZiB97y2+66aZseUSxZzzjGarQAGiJCM9ih1GzA7SCeH+N9gcjDWU2b95ccjymeccXbeojig0KlfkFa9asSe0udobG80r/ts63a9eu7Ht8fA6Pv+2lS5dW1XMY+C81vxJefvnl6bTTTkv/8R//UVLNcuGFF6Y3velNtV4dMEKx9/KJJ57Iljc+//nPb9ulnAUnnnhiyfH4gvHv//7vFS/35S9/OQvffvM3fzM7ftJJJ2UhWvjpT3866Pz79+8fOJ8P/gA0S7yvxcqMVgVohffAGMSzYMGCmi8bO8PjC3YxVWj1FTsDoxotKuo7SVQtxXT1RYsWtXpTGIUIb2NlSqHaNP7m47tEBGmt2MEOnarm+s3vfe976T3vec+gMdfxx9cJe1KgW9x4443pwx/+cPr85z8/6ENvXuxJjuqsuXPntixEG81e2+LKsgjjlixZMuT577333vSFL3yh4mMCAPWuQosdXK0WywOjWnsklyvXw4v6imCyEwOLeH4UV9DRmSF//ncYn5cjeAcaWIkWb8r5XgkhUmyTe6B5osdYoUl/7FV6+tOfPuR5i6dbtnIPebGoLvuzP/uziueLZZzFoX0sJbjyyiuzCryh7kv+MgDQaPmlkBGUNGv5W1QKFcTn9GjhEBVP1Yr30/xAgbi8fkn1F1XysTMwqtHaPZQqfl7FcySCtPiMReeJ312htUteVBnGTuuR9iuGXlPzO/u5556brr/++vSpT31q4APCzp0704oVK0Y0MRAYmeKKsliuOVyIVqxd9n7GB/PFixeP6LKxd9ySAgDaKWwobhgf5s+fn1WAN0P0EI1KuOJAr5YQLQYK5AMdSzkbJ/q55nu6tqPYSVv8vI7hUNHOR7jaWWLZZvQarvS7Pv7449t6yAW0i5pfAa+77rqsj9Ezn/nMrGQ9pnMWlnLGcAGgNSEaANA+VWjNDKGOOOKIUS0tzW9/TPmcPHly3baPzpQPgSNoHaqaifYUq8giIMuv3ojKs/yOgFb3dISurUSLJWQxVOCWW27J/o0qtDe+8Y3pda97XfaGCzRH7OGOaTrxphdvjkPZs2dPtpzE5B0AaMyX1HwIFVVgzazoiN5lcXvFLVdim6qp2o5hBPF5frhQjt4U3+1iiV9xlWMsQ40qunZZ2cDwYqlmPlCPv++FCxdmk4SLf7dRdRjhmipUqGOIFl/WY8nY17/+9Sw0iwPQGvFhOXpqxBtgNASNN8FyvQzuuOOO9I1vfCP7IF2oHAUA6iO+eOb7BRdPsG+GWF4XX3yLpz5Gj7OY0llp6V2+F1qcvxOWGtK8arTiFQ8RyMRnzhhaRXuLcDw/CTZ68sXrQoSg8T3ikUceKRlEEss+4/uECfdQp+WcUcnSDlOHgMFLOoeqRosPPvHmGMM/NAwFgPrKV6FFuNCKpZD56rF4788HZHmFZvHFIkDT84qCGByXX82QD2ZoP7H0Nj77FysEZ4W/7xjCle9PXPjOMJIJv9Aran6HfOtb35r1Pmv3aTLQC6rpi1Y4PT7QN6vBMQD0gqjIibYJraxCK4gvxPk+RxHwDdfjKCpViicwBks5yQcv+c+PMYgimtXTnuJvPvqb5b+vRyuYfPulCM3zyzfjNS2WgQJ16ol27733pttvvz1961vfSieffPKgUt6vfOUrtV4l0KAQLT7kFPZCRz9De5YBoH5iWmGxqNiJyp1WiQCsuL9ZrCCJL8RDVaLnq9Bih5sex+RFj7/169eXVCfFc9+k9PYUf9f5ARDxnX2ogD/6o+3atSvrj1hcbRihfD6YB0YQosWL6EUXXdSYrQFqEj0NYs9z/F0WRtnHHuXCG2f0OSgXuAEAvxLvmREIjGSVRb4aJ0KsVjZcLyy9K64ui51sQw05yG+/huKUE8+feG4XL+OMoCaCF0YmdmzHZ/daXzMiGI9Aszjwysv/rNBHeajbiW2Jne0rV64sqVyN147hBpPFd5CobjPJl15Tc4h20003NWZLgJrFm+K1116bvYkVrFmzJjstT4gGAIO/kEZP0eGWPFYrvqC2OoQqbEPxUqwIB6sJCOOyhR1ykJcP0eJvxpLO0Ykq0cJQkGrE33EMFMsPMqkk+p4NF4aFqECNQGzdunUDp0Xl4XC/4/hZLGlftmxZyXcR6HbWdkGHq+ZNKz4YH3fccU3ZHgDoFBEK1CNACxFAjR9f8/7puhtpNVz0RhqqYg3i86aprfUX0zDzfQnLidepaPhfa4AWAV1fX19V543lnrUu34ztKQwxg14xonf6v//7v09f+tKXsj+YfLnofffdV69tA0Ygeh6cdtppA8fjg/Szn/3sljU6BoB2FFUd27Ztq8t1RXh25JFHpnZQ2JZY8lWtCM/aZftpX1GpFD33ag1yGFqETxGkVVoxEkNCivsdViOqy6LfWbUK0ztjWedwy0XLVfTGIIOoeGvlcnZo2xDthhtuSH/2Z3+WXv/616d//Md/TJdddln65S9/mQ0ciMmdQGvNmzcvveENb2j1ZgBAW8tProwvf9EovdYvgbEcK3ZgtVMVV0xTjP5o8eW2E7ef9q1GO+GEE7JeaCqPRi7C+xj+VdyXMU4bqtIv/o6Ll1kWwvLojTyU+HuOv+tah4rF9cbyzFimOVxYGkvGi4O2GGQWvdHyk1yhG9Ucon3sYx9Ln/rUp9JrX/va9NnPfjb96Z/+abZM7Oqrr84+jAAAQDuLACD/uTW+wLa6p1m9RBAYX2g1/KbeIpypdnkg5cWSyYcffrgkpIpqtAi98kvC47WqXN/GqPpq1CTg+B1Xuu54bYmKteIwNYK+ON1ET7pdzT3RYgnnWWedNVAiWkjRf//3fz998YtfrP8WAgBAHUXlR77ZvrYHQDNEUJZfZhmvR/lqsxCn5Zv7x2tVowK0akVYFks/8yLwq2UpKPREiBZlo4U9d7F2++67787+PyaF1KsxKwAANMqmTZtKjk+dOjXbOQzQDFH5mg/CYklkcd+zKFbJV8xGeBW96dpBVCRGG5liBg3QC2oO0V784henr371q9n/Rz+0P/qjP0oveclL0sUXX5wuvPDCRmwjAADURfT62bNnT8lpqtCAZir0YMz3LFuzZk0WREVlWkzjzF8mqr9q7XPWSDGQJB8GRg+3uB8KbOhWY/prfHZHqhyHwnrtm2++OX3/+99PT3va09Kb3/zmrOFku5fvR/IfzRut5wcA6C2x3Kh4Kmd8pj3xxBNNlQOaLirNYrJlPtSPJZz5aZyxBLQdA/8I/WLQYH4ZZ6xgM2iATlJtVlRziNbphGgAAL3pwIED6aGHHiqpkIilUfklSQDNEK9F0RYpKmSHE9Ve0UqpXcP+qD7LDxoIS5cuNWiArsuKap7OWVivfc8992SjbfN/KJdccslIrhIAAOomPqPGF9PiAQJR2VEcoMUX0m6ZyAl0nngNikmbjzzyyJDLH6NaNs7TrgFa8aCB6IeWr/yNirRmbns8XjHptJ0fLzpbzSHa1772tfS6170u+xAS6VzxkzP+X4gGAECrA7THHnusYnXHrFmzBlqUALTCpEmTst5i69evL/vzCKc64XWqMGjgqaeeKlnqGf3Rmi2GxUQVXDv1j6N71Pys+uM//uP0hje8IQvRoiJty5YtA4f89BAAAGi2WIpRKUAL7dhfCOg90TssqrnKvUZ10nLIcoMGWiFe/6MKrsc6V9GuIVokye94xzuydBcAANpJfGnatGlTxfPFF9NyX1oBWrWss1i8PkXPxk5SmCDaDsMGd+zYkQ1tEKRRbzXXhZ533nnphz/8YTruuOPqvjEAADDaCoRocl0svtAVWpDEv/HlNPr0ALSLKVOmpGOPPTZt3LgxW74ZAVonLkccN25cdj/WrVs36LW4GcNjinu2x2q5CRMmZBVy0NQQ7atf/erA/19wwQXpne98Z/rZz36WTj755OxJWezlL3953TYOAABqka9CiwDtaU97mibTQNuLhvhx6HSRERx11FFNv91du3Zl/TCLq89iGGKEkkcccUTTt4fuNKa/ivrGahPw+HASzQO7YWwpAACdZf/+/emhhx4qOW3hwoV6nwH0iPi+n58SGo4++mjf/6lLVlRVOhYlkdUc2j1AAwCge+WHXMWO4JjACUBviPAjdp7kxaCBagbOQCWdt8gaAAByYoduPkSLAC368wDQO6L6OCaeFosFeKtWrUr79u1r2XbRYyHaXXfdlb7+9a+XnPa5z30uaxoYjfr+4A/+wBMSAICW2Lp1a0lD6WAZJ0BvisEM+UrkWDkXPdMOHjzYsu2ih0K097///emBBx4YOP6Tn/wkvfGNb0znnHNOete73pW+9rWvpWuuuaZR2wkAAGVFhUF+oMD06dPTpEmTWrZNALRO9GtfvHhx9l6Qn+AZQZpWVDQ8RLv//vvT2WefPXD85ptvTmeeeWb69Kc/nZYvX55uuOGG9KUvfWnEGwIAACOdyJZfEaEKDaC3RZAWU0InT55ccvrevXuzHmlVzFiEQcanKm3ZsiUriSz47ne/m172spcNHD/99NOzJyIAADRTvgotKtDy1QcA9J7oi3nMMceklStXZlVoBTt37kwPPvhgFrRVEkNqZs+ene2cqeb8dLeqK9EiQHv00UcHxoffd9996dd//dcHfr5jx440YcKExmwlAACUERVo8Tm02BFHHOGLDgCZyCmWLl06aNBM9EaLYK3SId5n1q1blzZs2NCy+0AHhmjnn39+1vvse9/7XrrqqqvS1KlT0wte8IKBn//nf/5nWrZsWaO2EwAABslP5IyKgXwzaQB6W1QoR0XaaHawPPXUU9mB3lZ1iPaBD3wgjR8/Pr3whS/M+qDFYeLEiQM//8xnPpPOPffcRm0nAACUiMbQ0XKkWCy5yVcbAEAUAh199NGjCtLWr18/qIUAvWVMf43d9LZt25b1mMh/OIm9gHF6cbDWjrZv355mzpyZ3Y++vr5Wbw4AACMUX2TWrl1bctoJJ5zQ9p9HAWidWMa5Z8+eqgYLxFLOCM7yYvJn7LShe1SbFVU9WKAgrrSc6D0BAADNEF9+8tUAM2bMEKABMKxYYRfvF9WKNgH5HTZr1qzJKtq0D+g9VS/nBACAdhGT1WLYVbGYnAYA9RTvLTFoMe+JJ57IqpfoLUI0AAA6Tr4KLZpGT5s2rWXbA0D3mjdvXnbIW716dbZTh94hRAMAoKNEj5r8l5aoFBhNs2gAGM6RRx45qOI5WgusWrUq7dq1q2XbRXMJ0QAA6OgqtBh4pS8NAI0UO2oWLFgwaKBAIUjbvXt3y7aN5hGiAQDQMQ4dOpS2bt1aclp8oYnGzwDQ6CBt0aJFgwYuHj58OAvS9u7d27Jtozlqns4JAEB50eg+lnTEXumRmDp1apo8eXLdt6ubbNmyJfuyUsyUeACaGaQtWbIkey/asWNHyU6eRx99NFv2WU17gaiinj59evYvnUOIBgBQB9Gj67HHHhv19cQHc0sTy4twcvPmzSWn9fX1pYkTJ7ZsmwDoPRGSHXXUUenxxx8v6dEZQdratWurvp4JEyakY445xg60DqLuHQCgDuHOunXr6nJd8eE7X2nFr8Qe/6j2K5Zv8gwAzRBtBI4++uisinykDhw4kFauXGnCZwcRogEAjNKePXvq1gcl9mJv27atLtfVbTZu3FhyPPbcj+bLCwCMNkiLSrIpU6aM+Dpix1lUsucrrWlPlnMCAIxS/oNvfKieNGlSTXuiDx48WDJ9MpZ0VtNTpVfE1LP85LOoQvMYAdBK0dNs6dKlacOGDdlOtWr6osZ7frz3F3vyySezauv58+d7b2tjQjQAgFGID8L5yrF58+Zlh2pt374966tSEFVtERhNmzatrtvaySJYLDZ+/PhB09EAoFVB2sKFC2uqPnviiSey9/98xXUEadEf1dTp9uS3AgAwClu3bi3Z6xx7j2fPnl3TdcyYMWNQc/x8aNTL4gtFPqiMKjRfMADoRPH+FYMJ5s6dO+hnEazFlM/iCnXah08eAAB1nhYZVVK1iODtiCOOGPQhOt9Ev1flA8X48pF/vACgk8R7/4IFC9KiRYsG/SyWhf7yl7+sW79V6keIBgAwQjFNKx90jTTcieq1fGWVarRfDVrYsmXLoMcqls4AQKeLzw3RUy3/GcDkzvYkRAMAGKF8FVoMExjptMgIhfLLQCM8ihCp1x/j6B2TX8oJAN1i+vTp6bjjjksTJkwoO7kzvzOJ1hGiAQCMQOwh3rFjx6C9yaOZqJWvYosPz9FzrZeXy+ar8WK5bL5/HAB0usmTJ2dB2pQpUwb9bM2aNWndunVVTf6ksYRoAAB1qEKLZRizZs0a1XVGJVsMGSgWIVKvfmiOYQL5xsrlmjADQDeISrRjjz0222GUF5M7V69ePag6m+aqrestAABZqJVfWjFz5sy69OmKpYrFFW7Rc+0Xv/hFaqaopoulJdHsuFUTMOMxji8MxWKp7EiXywJAJ03uXL9+/aD3wRg6FJ8JRlP1Xm9Pf/rT22p7Gk2IBgBQo/gQm6+Qqte0yGnTpmVLOooncrWiL1osI40P8uWmhjXDrl27Bk0lU4UGQC9N7oz2BU8++WTJz1SitZblnAAAo1zKGf1LyvUwGekH53ZpnB/VdvmwsFnye9/ji0R+qSsAdLPYQXfMMce0rCqcwfwmAABqsG/fvqxKqhFVaAXRWy2WU7bjstVmiAq0nTt3lpwWwWIvLRcBgBA7kGLggKE67cFyTgCAUVShRR+06IdWTxEWxZ7nCOyaXQkWgwyKe7LF/Y1llM0MsPITOeMxnj17dtNuHwDaSbR5OP7447OdTJZztpYQDQCgSvHBNXqFFYtwpxHLLCK0ig/NzRb3pThEO3DgQNYDrt5B4VAiNMw/xlHpZykLAL0s3gcN12k9n0YAAKq0bdu2QU3+u61CKj6g5/u75SvDGiluK5aRFoeJ9V4uCwAwEkI0AIARLuWMvmWTJk1K3SY/2GD37t1pz549Tan0yz/GUQE3YcKEht82AEAlQjQAgCpEiJQPkrq1Qqqvry+NHz++6dVosYwzX+kX/dgAANqBEA0AoAr5CqkImWJiVrf2XckHhLGUtZFDDmIJ58aNGwdV+rWiLxwAQDlCNACACqI6qlyz+2ZOrGy2/P2LkCsfJNZTDDPYv39/yWmq0ACAdiJEAwCoIAK04mb33ThQIC8q7fITOSNEyz8O9ZKvQosKtGnTpjXktgAARkKIBgAwjHIVWNEzrBea3ecHDMRyzu3bt9f9dqLXXAwvyN92N1f6AQCdR4gGADCMCHf27dvXEwMF8qZMmZKmTp3a8AED+Sq0clVwAACtJkQDABhGvgpt4sSJPbXMMF+NFqFifkrpaBw4cCAbWpC/zRhuAADQTnw6AQAYQrnli90+UCAvlq5GZVijqtHyIWU8tt3ebw4A6ExCNACAIWzZsqWkkX4EPLNmzUq9JO5zfvlqVI5FwDhahw8fHhSiRYCWD+0AANqBEA0AoMqBAtGnqxcDnnz1XbnHZqRTTw8dOjTotgAA2pEQDQCgjJ07d2b9uor1asBTrtF/hGhRSTZSEcTll4VOnz49TZ48ecTXCQDQSEI0AIAy8pVWEe7EtMpelR8wUK5fXK0hZX7qaf42AADaiRANACAnKtB27NjR0wMF8iJAnDp1at0GDOQvO2nSpKwSDQCgXbVFiHbjjTempUuXZnt4zzzzzHTPPfcMed5Pf/rT6QUveEHWdDYO55xzzrDnBwAYbRXa2LFjBy1n7EX5SrE9e/ak3bt313w9e/fuzSrR8tfdyyElAND+Wh6i3XLLLWn58uVpxYoV6b777kunnHJKOu+889KGDRvKnv/OO+9Mr33ta9Mdd9yR7rrrrnTUUUelc889N61Zs6bp2w4AdJ/o1RVTOYtFgDZu3LjU6/r6+tKECRNGXY2WDynjse21qacAQOcZ0188t70FovLs9NNPTx/96Eez49GgNoKxt7/97eld73pXxcvHRKeoSIvLX3LJJRXPH7074oNwjGaPD4IAAPnPCo8//njJacuWLevpfmjFnnrqqbR+/fqS02qtIMt//Jw7d25asGBBXbYPAKBW1WZFLa1E279/f/rRj36ULckc2KCxY7PjUWVWjVhCEH1LhpqWFQ1r48EoPgAAVFslFeGZAO2/xM7LfGgWoVgthzwDBQCATtDSEG3jxo1ZJdn8+fNLTo/j69atq+o6rrzyyrRo0aKSIK7YNddck6WJhUNUuQEADLXzLd+ra6gddb1q/PjxdV16GZ/P8ktEAQDaUct7oo3GBz/4wXTzzTen2267LRtKUM5VV12VleMVDqtXr276dgIAnSHfC81AgfJih2dM0xytCM8s4wQAOsX4Vt549L+IRrL5vhpxvNIHquuuuy4L0b7zne+kZz/72UOeLz7g1eNDHgDQ3aIvaz5Ei6WLEaQxuBrt+OOPzyr3RtpeN5aExmc0EzkBgE7R0hBt4sSJ6dRTT0233357euUrXznwATaOv+1tbxvyctdee236y7/8y/TP//zP6bTTTmviFgMA3Sr6pkabiWKWcg4twq+hVgIAAHSjloZoYfny5enSSy/NwrAzzjgjXX/99WnXrl3psssuy34eEzcXL16c9TYLH/rQh9LVV1+dvvCFL6SlS5cO9E6bPn16dgAAqMdAgWnTpqlmBwCgfUK0iy++OBuVHsFYBGLPec5z0je/+c2BYQMxYr54GcXHP/7xbKrnq171qpLrWbFiRXrf+97X9O0HADrf3r17s4nfxVShAQBQbEz/SBtZdPBSjWgQHEMG+vr6Wr05AEAbWLt2bdq0adPA8ejZeuKJJ+qHBgDQA7ZXmRX5ZAgA9DQDBQAAqIZPhwBAT4s9jhGkFbOUEwCAtuuJBgDQKDGsKA7D2bp1a8nxGFQUE8QBAKCYEA0A6EoRjj3xxBM1X04VGgAA5VjOCQB0nZibFFO/azV+/Pg0Y8aMhmwTAACdTYgGAHSdHTt2pIMHD9Z8uXnz5qUxY8Y0ZJsAAOhslnMCAF1n8+bNgyrMpk6dOuT5IziLXmizZs1qwtYBANCJhGgAQFfZt29f2rlzZ8lp8+fPT7Nnz27ZNgEA0Pks5wQAuroKbdy4cWnmzJkt2x4AALqDEA0A6BqHDx/OpnIWiyWaY8f6yAMAwOj4RAkAdI1t27alQ4cOlZx2xBFHtGx7AADoHkI0AKBrl3JOmzYtTZo0qWXbAwBA9xCiAQBdYc+ePdmh2Jw5c1q2PQAAdBfTOQGAuujv76/qfGPGjGnI7eWr0MaPH59mzJhRl9sCAAAhGgAwKtGDbPXq1Wnnzp1VnT+WVy5evDhNnTp1xLcZgdmGDRvSwYMHhzxP9EKrV2AHAACWcwIAo7J+/fqqA7Swb9++9Pjjj2eTNEciLv/kk08OG6CF2bNnj+j6AQCgHCEaADCqKrQtW7bUfLkIwLZv3z6i28wv2yynr68vTZgwYUTXDwAA5QjRAIARiwCt2l5oIwnD8qJ6rVJoN3HixLRw4cIRbRMAAAxFTzQAYEQiPMsHYdHIf8GCBWXPH0s+165dO3B89+7dae/evWny5MlV3+bWrVsHLQM99thjsyECIXqgRQWaXmgAANSbEA0AGJEIxfbv319y2ty5c7PBAeVEuPXUU0+V9DKLEG7RokUjDu2mT5+epk2bNqLtBwCAWljOCQCMyKZNm0qOR0XZcBM3x44dO6jZf1SWRV+1auzZsyerXMtP4AQAgGYQogEAI5qQmZ/IGYFWpWWU+dArlmZGkFaNfBVaVLbF8lEAAGgGIRoAULN8oBVVZrNmzap4uQi+YnJm/roqDSeIJaDbtm2rObQDAIB6EaIBAKOekBnLNCNIq0a+Gi2q2nbt2jXsZaJarThoi/AsvzQUAAAaSYgGANSk3ITMWnqTxSCAiRMnDlvZVmmgQFSzFSZyAgBAM/j0CQAMKaZvxjLK4ub/+WWV0ZdsqImc5UQV2Zw5c9LatWsHTtu+fXt2vNzyzFjKmZ8CaqAAAADNJkQDAMqK8GrlypXZv8MZSaAV/dPWrVtXskQzP+1zKBHYDTcFFAAAGsFyTgCgrAi1KgVosSxz+vTpNV/3uHHjqhpEUI6BAgAAtIIQDQCoanhAOUceeeSIA625c+dWPYygeLrnSMM3AAAYDcs5AYBBokdZvgpt5syZA4FZ/Bu90KLB/0jFssxjjz02G1RQ3HNtuAAtJnJGFRsAADSbEA0AGCTfnyx6kB111FF1v50pU6ZkBwAAaHeWcwIAJfbs2ZMdisU0TQAA6GVCNABg2Cq08ePHj2rZJgAAdAMhGgAwIPqgbdu2reQ00zABAECIBgAUiYmc/f39A8cjPIsQDQAAep0QDQDIRHi2efPmQRM5YzknAAD0Op+KAehqhw4dqnieqLYaO3ZsT4Zmhw8fHji+Y8eOdODAgZLzqEIDAIBfEaIB0JX279+fVq1alfbt21dViBYVV4sXL+6Z3l/bt29Pa9asGTZknDJlSpo6dWpTtwsAANpV7+12B6AnREBUTYBWqMjaunVr1g+sV4YHPPHEExWr9ObMmdO0bQIAgHYnRAOg6+zduzft2rWr5stFP7DipvrdKsLC4mWc5UQftL6+vqZtEwAAtDshGgBdZ6QVZRG+7dmzJ/Xa8IC8cePGpSVLlvRknzgAABiKnmgAdJWosIqlmfnm+PPmzSt7/pUrV5Y004+AqZv7gJUbHnDMMcekyZMnl1Sh9UpvOAAAqJZdzAB0XUiU7/UVvb0mTJhQ9pCfPrlt27aqJnp2qk2bNpUcj/Bs+vTpJY+JAA0AAAYTogHQVfJLFaOqbNKkSUOef/bs2SWhUSx37NYBA+V6xUXAKDQDAIDKhGgAdI39+/cPConylWbVNNCPEK0bBwzkA8bofTZz5syWbQ8AAHQSIRoAXSNfQRaN8auZMBnVaMX27duXdu/enbpJLFEt1yvO8AAAAKiOT84AdIVyyzBnzZpVVUg0bdq0QUs+K02w7DTx2MTQhVqq9AAAgP8iRAOgawYKHDx4cEQhUfQEy1ejbd++fdD1dXLAmA8Fo0IvhggAAADVGV/l+QCgJJSJKZaNXPIYUyOrrSQL+Sq0KVOmZNdRrQjR1q9fP9ALLf5dvXr1sEMJmqHWxyG2O5Zt7tmzZ+C0CAOjX1x+oAAAAFA9IRoANXvqqafShg0bGn47EdItWbKk4vkOHDiQVaIVy1eWVVJosl/cNyyGFOQHFbRCBGKLFy+u6rzxe4nfT6VgLqaWAgAA1bOcE4CaG9Rv3LixKbcVgVa+gqqc/FLFqNoaydTJdu0RFlV21TwO1f5u4n7GElYAAKB6QjQAahK9wvIN6hupUoP/cgMFIkCLyrJaxRLQGTNmpHZUzaCDeBwKy1GHEstTY3koAABQG8s5AahJPrCKUCamW9bL3r17S3qtxe0deeSRQ/YEKzcAYKQVZVGdFctHowJu3759qZVqfRzKDQ/IL9uMQQK19FcDAAD+ixANgKpFsJQfJjB//vxs0mM9w6NHHnmkZIli9DsbanlmPjiKarI4jFRUsLVD0/14rB9++OGSxyGGOQzV623nzp2DlnwuXLiwrgEnAAD0MruiARhxFVoETvVe/liu6X3+douDpnzj/3bta1arqPCbPn16yWmbNm0acrlmPkyMyxseAAAA9SNEA6Aq5XqPRVVUIxrU56utylVZlQuOChM2u0U+EIwqvZjUmRePTX46aVTTGR4AAAD1I0QDoCoR0sSSwmJDLS0crQjC8n278oFZDDeI3mX57emmfl9R5Rd9zPLVaI2aTgoAAAyte75pANBQ+Sq0WCoYSwYbIUKgfEAXgVnxUsboD9asUK9VopIsX42WH6QQYWL+dxPDA0YynRQAABiaEA2Aig4cODBouWCjA6v89UdwFAHSUNVX0T+sUaFeK+WXzOancMZjkg8Tu6UvHAAAtBMhGgAV5SudolKsnhM5qx0wEOFR9P+KHmn53mDdGhyNHz9+0NLMwuMQh/zyzpjGGY8dAABQX+PrfH0AdJmofMr3HotQpxnLBaMKa/fu3QPHYxLnQw89VDZoqveU0HYSQwKKfwdRlVfucejmMBEAAFpNJRoAw4rgKj8Zs1m9x8oNGBgqOOrmSZRTpkzJDpVEmNjoCkEAAOhVQjQAalrKGX3Hqgl0GjVgIC/Cs24bKDBUNVqvh4kAANBKQjQAhhQN64ub+ZdrdN9oRx55ZDY0oJxYUrp48eI0YcKE1O2iKm+4kCwq0ObOndv07QIAgF6hJxoAQ4o+XNETrSACnFmzZjV1GyIoW7p0aTp8+HDJthQq1Xql8iru56JFi9KCBQsGPQ7xs2qWvQIAACMnRAOg6qWc0bw/+m61gpDoVzwOAADQGj6JA1DWnj170t69e0tO64XeYwAAAOUI0QCoqgotKtCG6k0GAADQ7YRoAAwS/ceiH1orBwoAAAC0EyEaAIPERM4I0opZygkAAPQyIRoAFZdyTps2LU2cOLFl2wMAANBqpnMC9LiDBw+mzZs3p3379g2ctmvXrpLzqEIDAAB6nRANoIf19/enVatWZZM4hzJu3LjU19fX1O0CAABoN5ZzAvSwqDgbLkALM2fOTGPHersAAAB6m29FAD0s3/ssL8KzuXPnNm17AAAA2pXlnAA96tChQ9kUzmIzZsxIkyZNKlnGaaAAAACAEA2gZ23bti3riVZs8eLFafx4bw0AAAB5lnMC9Kj8Us6oOhOgAQAAlCdEA+hB+/btGzRQYNasWS3bHgAAgHYnRAPoQfkqtOh/Fv3QAAAAKE+IBtBjog/a1q1bB1WhjRkzpmXbBAAA0O6EaAA9ZufOnengwYMlp82ePbtl2wMAANAJdJCGLq002r9//6DJi+1mwoQJ2TLCbhe/hwMHDqTDhw+ndrB58+aS45MnT84OAAAADE2IBl0mwprHHnssaxzf7mL54Pz589PcuXNTt4qKr0cffbStfx+q0AAAACqznBO6TFQZtXNgk6/QWr9+fRb8dau4f+38+4ggc+bMma3eDAAAgLYnRIMus2vXrtRJIkjrtG2u5b5t3749tbO+vr40fryiZAAAgEqEaNBloc3evXtTJza670Zxvw4dOpTa1bRp09LChQtbvRkAAAAdQfkBdJFYNphvXn/iiSe2XaXRpk2b0rp16waOd2sl2rZt20qOT5kyJR133HGpnZZyAgAAUJ32+mYNjMqePXsGTb+MQ7uZPn16yfHoiRbTRCdOnJi6RYSZ+aWc0XtMcAUAANCZLOeELg7RovKpHU2aNCmNGzeu5LRuq0aLpZz5qkAN/AEAADqXEA26SKeEaFGNFf24ujlEyy/lnDp1altWBQIAAFAdIRp0iah6yg8VaNcQLZQL0WIwQrf8Lnbs2FFymio0AACAziZEgy4aKpAPoSZPnpw6JUSLvmhx6NalnH19fS3bHgAAAEZPiAZdupQzmvS321TOXumLZiknAABA92nfb9hAV/ZDy/dFK55g+dRTT2XHo4Juzpw5ZUPAqPDavHlz2r17d9su/4xKtGKWcgIAAHQ+IRp0iU4L0UI+RNu/f392iH5icX+WLl066DIbNmxIGzduTJ1EiAYAAND5LOeELtBpQwUKpk+fPmw1VwRqxaLyLKrQOkkEhe28rBYAAIDqCNGgC+QDtHYfKlDcF224hvv5HmmxhDPfsL/dHXnkka3eBAAAAOpAeQR04VLOck3729WSJUuyqrMIAmNpZ3EgGKfPnj174Hgs8ywWzfqLf95Oxo4dm1XadUKYCQAAQGVCNOgCndgPrThsimq0OETwt3bt2pJKtFjCGUMIyoVos2bNUukFAABAU1jOCV2gk0O04XqkHTx4cKAvWvy7b9++kp/PmDGjqdsHAABA7xKiQYc7dOjQoHCpU0O0iRMnDmrCH0s6y1WhRdVap95PAAAAOo8QDTpcpw4VKCeWbear0QrDBQphWkGcr7DMEwAAABpNiAZdtpQzArToM9appk2bNihEi4mc+RDNUk4AAACaqeXftG+88ca0dOnS7Iv/mWeeme65554hz/vAAw+kiy66KDt/VKBcf/31Td1WaEfd0g+tIF+JFstVN27cmA0YGO58AAAA0LUh2i233JKWL1+eVqxYke677750yimnpPPOOy9t2LCh7Pl3796djjvuuPTBD34wLViwoOnbC60UjfXjb6DcoZtCtAkTJmS90YrlXxOmTp06qHcaAAAANNKY/nx5RxNF5dnpp5+ePvrRj2bHY8nWUUcdld7+9rend73rXcNeNqrRrrjiiuxQi+3bt6eZM2embdu2pb6+vlFtPzTLunXrsmqsaixbtqzjg7Qnn3wybd68ecifH3nkkdkBAAAARqvarGhsK6tqfvSjH6VzzjnnvzZm7Njs+F133VW324mphfFgFB+gkxw4cKDqAC2WOU+aNCl1unxftDz90AAAAGi2loVoEQpEr6P58+eXnB7Ho+qmXq655posTSwcotINOrnn2XBimWMnDxUo7nc2bty4sj+LkLBTp48CAADQuTr/23YFV111VVaOVzisXr261ZsEow7RIijLHyJAW7RoUeoGEaAtWbJkYNJo8X2M06PiDgAAAJqpZZ25586dm31RXr9+fcnpcbyeQwOiaqUblrfRu/bu3VtyfM6cOWnhwoWp28WSTcs2AQAASL1eiRbT90499dR0++23D5wWgwXi+POe97xWbRa0fSVapw8NAAAAgE7Uskq0sHz58nTppZem0047LZ1xxhnp+uuvT7t27UqXXXZZ9vNLLrkkLV68OOtrVhhG8LOf/Wzg/9esWZPuv//+rH/S8ccf38q7Ag0bKnDw4MGS04RoAAAA0GMh2sUXX5yeeuqpdPXVV2fDBJ7znOekb37zmwPDBh5//PGSJulPPvlkeu5znztw/LrrrssOL3zhC9Odd97ZkvsAzaxCi7+HqOIEAAAAmmtMf39/f+oh27dvz6Z0xpCBvr6+Vm8ODGvDhg3ZoSAa6x933HEt3SYAAADoxayo66dzQifTDw0AAADagxAN2pgQDQAAANpDS3uiMXqPPfZYKl6RGz2zZs2alZUh0tlioEB+qMDkyZNbtj0AAADQy4RoHS6mmebb2u3YsSNNmDAh659Fdw0VmDRpUsu2BwAAAHqZ5ZxdaufOna3eBOocokUV2pgxY1q2PQAAANDLhGhd6sCBA63eBEZJPzQAAABoH5ZzdrgFCxZk/27YsCEdOnRo4PR8Ly06z969e0uO64cGAAAArSNE63Bz5szJ/o1lfk8++eTA6SrROluEoPnfoUo0AAAAaB3LObtEDBIophKtu5ZyRkhqqAAAAAC0jhCtS4wfP35QiJaf2knnMFQAAAAA2osQrUsr0YJqtO7ph2YpJwAAALSWEK1LjBs3btBpQrTOZTInAAAAtBchWpeIpX75ajTDBTqToQIAAADQfkzn7LK+aMXhi0q0kTl8+HC2nDL+LSca/JdbPlsP+/fvT5s3by45zVABAAAAaD0hWheJYKd4GaBKtNrFY7Zy5cqKj92SJUvSrFmz6nrbGzduTOvWrRt0uqECAAAA0HqWc3b5hE5qs2XLlqrCxw0bNtT1dmOS6lDXaSknAAAAtJ4QrYtDNJVoo2/oP9yyy0OHDtXtdodbPjp9+vS63Q4AAAAwMkK0LpLv06USbWRhVrGxY8dm4WQ+oCx33nqHd/H7nDdvXpoxY0bdbgcAAAAYGT3RuohKtNGJyrL8Y3bssccOLKd85JFHSoKz+P9p06bV5bbzgVxfX186+uij63LdAAAAwOipROviSrQIhYZaIshg+/btG3Ra8VTMaPA/kqWf1chflz5oAAAA0F6EaF2k3JJDSzpHXg0WAVos5xwqRCsXuo10qED+toVoAAAA0F6EaF1k3LhxacyYMSWnCdGqlw+y8qFZ/nicPwKwetxu/nrytwUAAAC0lhCti0SAlq9GE6I1LkSL4Kse1Wj5pZyxLLdcVSEAAADQOkK0Lu+LZrjAyJdU5kOzclM66zGh01JOAAAAaH9CtC6jEm1kImzMD2Eot6Sy3JLO0TJUAAAAANqfNWNdRiXayOTDsOgvV25JZYRoO3fuHDi+ZcuWtGvXrlHdthANAAAA2p8QrcuoRBuZcks580MaCqcXO3To0KAQbLQMFQAAAID2Yzlnl4doKtGqU6kfWrOqxCZOnGioAAAAALQhIVqXL+dUiVbfEG3SpElpxowZDduOuXPnNuy6AQAAgJFT8tJl8lVMsdwwGuaPHSsvHUo8Pvv37696SeXRRx+d9UWrd0AZt6kfGgAAALQnIVqXV6KFCHtimSDllZuwGRVnQ4leaY2sRgMAAADaj/KkLhMVZ/mG+Pqi1RaiRYCmcg8AAAAoJinoMhGg6Ys2+hANAAAAoJgQrQuZ0NmYoQIAAABA7xKidSGVaNXr7+8XogEAAAAVCdG6kEq06sVjE9M5iwnRAAAAgDzTObtQp1SixXbt2bMnqwYriCmilUKsQ4cOpd27d5dcbqTi9ovFQIFyE04BAACA3iZE64FKtHYM0fbt25dWrlyZBWJ58+bNS/Pnzx+yciwu16jqugjw8tNNAQAAACzn7EL5Sqp2XM65efPmsgFa2Lhx46AllgVbt25t6P2xlBMAAAAoR4jWA5VoEUgNFVi1Sr6Zf7FYprl///6qll/W2/Tp0xt6/QAAAEBnspyzB0K0wpLOcePGpXYxVEhW/PNyVWGxDDR/X6OP2WjFdcycOTPNmDFj1NcFAAAAdB8hWheKsCxCoeIlkRGiTZo0KbWD2K78ksz89ubDsqEq1I466qg0bdq0Bm4tAAAAgOWcXaud+6KVq0LLV4CVC9HicvmJnO0SDAIAAADdTYjWpdp5Qmc+IIvAb8qUKRWDtvzlouKu3NJVAAAAgHoTonWpfLjUTpVo+TBs4sSJ2WG485QbRqAKDQAAAGgWIVqPLOds50q0CMPygVhME81vc/5y5QYPAAAAADSCEK1LtXMlWn6pZgRo+Uq0cucrF74BAAAANIMQrUu1ayVaDAYot5xzzJgxwy7pLHc5IRoAAADQLLqy90glWlR1PfLII2nmzJlp7ty5WWjVKNu2bUubNm3KlmTmRRh2+PDhsmFY/FtcfbZu3bq0cePGgcuZzAkAAAC0ihCtRyrRCo354xAVXxGmNUKEYKtXr676/BHmFbY1QrEdO3YM/CxCuHJBXBg7dqzJnAAAAEDTWM7ZpSJgGqrabNeuXQ273d27d9d0/gjOCttZS2VZDBVoZDUdAAAAQDEhWpeKSq05c+aU/VkjhwzkhwFUMm/evIH/7+vrqzpIK74cAAAAQKNZD9fF5s+fny3bjN5ixdVnjRwykA/opk+fnmbPnj1kNVlxaDZu3Li0bNmybFvzfdOKTZkypew0TwAAAIBGEaJ1sVjuGIFTVKQVh2jNrESbNm1aTf3XooJuxowZDdgyAAAAgJGznLMHhwxEJVp+0mW95AM6FWMAAABANxCi9eikzkYs6YxgLh+ilbttAAAAgE4jROsB0WssP8myEUs64zrzFW4q0QAAAIBuIETrARGgjR8/vikhWv52I8ADAAAA6HRCtB7ui9booQJRhZavgAMAAADoRKZz9oh8JdrOnTvrXiW2Y8eOkuP6oQEAAADdQojWI/KBVgRe+dCr3vRDAwAAALqF5Zw9ohVVYSrRAAAAgG4hROsR06ZNa/ptTp8+vem3CQAAANAIlnP2iClTpqQlS5akzZs3p8OHDzf0tsaOHZtmz56d3SYAAABANxCi9ZBZs2ZlBwAAAABqYzknAAAAAFQgRAMAAACACoRoAAAAAFCBEA0AAAAAKhCiAQAAAEAFQjQAAAAAqECIBgAAAAAVCNEAAAAAoAIhGgAAAABUIEQDAAAAgAqEaAAAAABQgRANAAAAACoQogEAAABABUI0AAAAAKhAiAYAAAAAFQjRAAAAAKACIRoAAAAAVCBEAwAAAIAKhGgAAAAAUIEQDQAAAAAqEKIBAAAAQAVCNAAAAACoQIgGAAAAABUI0QAAAACgAiEaAAAAAFQgRAMAAACACoRoAAAAAFCBEA0AAAAAKhCiAQAAAEAFQjQAAAAAqECIBgAAAAAVjE89pr+/P/t3+/btrd4UAAAAAFqskBEVMqOh9FyItmPHjuzfo446qtWbAgAAAEAbZUYzZ84c8udj+ivFbF3m8OHD6cknn0wzZsxIY8aMafXm0IPpdgS4q1evTn19fa3eHHqQ5yCt5jlIO/A8pNU8B2kHnoe02vY2eg5GNBYB2qJFi9LYsUN3Puu5SrR4MJYsWdLqzaDHxQtEq18k6G2eg7Sa5yDtwPOQVvMcpB14HtJqfW3yHByuAq3AYAEAAAAAqECIBgAAAAAVCNGgiSZNmpRWrFiR/Qut4DlIq3kO0g48D2k1z0HagechrTapA5+DPTdYAAAAAABqpRINAAAAACoQogEAAABABUI0AAAAAKhAiAYAAAAAFQjRoE6uueaadPrpp6cZM2akI488Mr3yla9MDz744LCX+exnP5vGjBlTcpg8eXLTtpnu8r73vW/Q8+npT3/6sJe59dZbs/PE8+7kk09O3/jGN5q2vXSnpUuXDnoexuGtb31r2fN7HWS0/vVf/zX91m/9Vlq0aFH2/PmHf/iHkp/HDK2rr746LVy4ME2ZMiWdc8456eGHH654vTfeeGP2fI7n45lnnpnuueeeBt4LuvU5eODAgXTllVdm77HTpk3LznPJJZekJ598su7v6fS2Sq+Fr3/96wc9p1760pdWvF6vhdTrOTimzOfDOPzVX/1VR70WCtGgTr773e9mXxLvvvvu9O1vfzv70HTuueemXbt2DXu5vr6+tHbt2oHDqlWrmrbNdJ+TTjqp5Pn0b//2b0Oe9/vf/3567Wtfm974xjemH//4x1nwG4ef/vSnTd1musu9995b8hyM18Pw6le/esjLeB1kNOJ99pRTTsm+6JVz7bXXphtuuCF94hOfSD/4wQ+yIOO8885Le/fuHfI6b7nllrR8+fK0YsWKdN9992XXH5fZsGFDA+8J3fgc3L17d/Yceu9735v9+5WvfCXbyfryl7+8ru/pUOm1MERoVvyc+uIXvzjsdXotpJ7PwbVFz704fOYzn8lCsYsuuqizXgv7gYbYsGFDf/yJffe73x3yPDfddFP/zJkzm7pddK8VK1b0n3LKKVWf/zWveU3/BRdcUHLamWee2f/mN7+5AVtHr7r88sv7ly1b1n/48OGyP/c6SD3F++5tt902cDyedwsWLOj/q7/6q4HTtm7d2j9p0qT+L37xi0NezxlnnNH/1re+deD4oUOH+hctWtR/zTXXNHDr6cbnYDn33HNPdr5Vq1bV7T0dKj0PL7300v5XvOIVNV2P10Ia+Vr4ile8ov/FL37xsOdpx9dClWjQINu2bcv+PeKII4Y9386dO9MxxxyTjjrqqPSKV7wiPfDAA03aQrpRLFGKEurjjjsuve51r0uPP/74kOe96667smVNxWLvYpwO9bB///70f//v/01veMMbsj2NQ/E6SKM8+uijad26dSWvdTNnzsyWJA31WhfP2x/96Ecllxk7dmx23Osj9fqMGK+Js2bNqtt7OlTjzjvvzNrOnHjiiektb3lL2rRp05Dn9VpII61fvz790z/9U7YippJ2ey0UokEDHD58OF1xxRXp+c9/fnrWs5415PniDSzKWP/xH/8x+6IZlzvrrLPSE0880dTtpTvEl8LoL/XNb34zffzjH8++PL7gBS9IO3bsKHv++GI5f/78ktPieJwO9RC9MLZu3Zr1YRmK10EaqfB6Vstr3caNG9OhQ4e8PtIQsYw4eqRFO4VYyl6v93SoJJZyfu5zn0u33357+tCHPpS1onnZy16Wvd6V47WQRvq7v/u7rJf4b//2bw97vnZ8LRzfsluGLha90aKvVKX12s973vOyQ0F8cXzGM56RPvnJT6YPfOADTdhSukl8ECp49rOfnb3pRHXPl770par28kC9/e3f/m32vIy9h0PxOgj0iuiX+5rXvCYbdhFfBofjPZ16+53f+Z2B/49BF/G8WrZsWVaddvbZZ7d02+g9n/nMZ7KqskrDpNrxtVAlGtTZ2972tvT1r3893XHHHWnJkiU1XXbChAnpuc99bnrkkUcatn30jlgmcsIJJwz5fFqwYEFWSl0sjsfpMFoxHOA73/lO+h//43/UdDmvg9RT4fWslte6uXPnpnHjxnl9pCEBWrw2xsCV4arQRvKeDrWKpXHxejfUc8prIY3yve99LxuwUutnxHZ5LRSiQZ3EXsUI0G677bb0L//yL+nYY4+t+TqiZPonP/lJWrhwYUO2kd4SfaZ++ctfDvl8iuqfKOkvFh/si6uCYKRuuummrO/KBRdcUNPlvA5ST/FeHF/2il/rtm/fnk3pHOq1buLEienUU08tuUwsM47jXh8ZTYAWfX1i58KcOXPq/p4OtYq2CdETbajnlNdCGrlS4dRTT80meXbia6EQDeq4hDP6+XzhC1/I1ndHr4A47NmzZ+A8l1xySbrqqqsGjr///e9P3/rWt9LKlSuzsdG/93u/l+2hHEkqD3/yJ3+S9bd47LHH0ve///104YUXZnsQo+9Kueff5ZdfnvUX+Ou//uv0i1/8Ir3vfe9LP/zhD7MwGEYjPmRHiHbppZem8eNLO0d4HaTe4gP1/fffnx1C9EuJ/4/Gw9G8PXqU/sVf/EX66le/mgW08RyMJcavfOUrB64jljJ99KMfHTi+fPny9OlPfzrr2fLzn/88a8C9a9eudNlll7XkPtK5z8EI0F71qldl76+f//znsx0Fhc+I0bh9qOdgpfd0qOV5GD975zvfme6+++7sORVBWAzyOf7447OhUgVeC2nUc7B4R9att9465Oe8jngtbPV4UOgW8edU7nDTTTcNnOeFL3xhNl664Iorrug/+uij+ydOnNg/f/78/vPPP7//vvvua9E9oNNdfPHF/QsXLsyeT4sXL86OP/LII0M+/8KXvvSl/hNOOCG7zEknndT/T//0Ty3YcrrNP//zP2evfw8++OCgn3kdpN7uuOOOsu+/hefZ4cOH+9/73vdmz69Jkyb1n3322YOem8ccc0z/ihUrSk77yEc+MvDcPOOMM/rvvvvupt4vuuM5+Oijjw75GTEuN9RzsNJ7OtTyPNy9e3f/ueee2z9v3rz+CRMmZM+3N73pTf3r1q0ruQ6vhTTy/Th88pOf7J8yZUr/1q1b+8vphNfCMfGf1kV4AAAAAND+LOcEAAAAgAqEaAAAAABQgRANAAAAACoQogEAAABABUI0AAAAAKhAiAYAAAAAFQjRAAAAAKACIRoA0JVe9KIXpSuuuKLht/PZz342zZo1q+G304nq9di0w2PcrOcTANC+hGgAQEO9/vWvT2PGjBl0eOSRRxp6u1/5ylfSBz7wgYbeBvW3dOnSdP3115ecdvHFF6eHHnqoZdsEABDGexgAgEZ76Utfmm666aaS0+bNmzei69q/f3+aOHFixfMdccQRqZMdOHAgTZgwodWb0RamTJmSHbrNoUOHskB57Fj7tQGgE3jHBgAabtKkSWnBggUlh3HjxmU/++53v5vOOOOM7DwLFy5M73rXu9LBgwdLltG97W1vy5bSzZ07N5133nnZ6T/96U/Ty172sjR9+vQ0f/789Pu///tp48aNQy6/W7t2bbrggguyMObYY49NX/jCFwZVPUWg8b//9/9OF154YZo6dWp62tOelr761a9WdR//4R/+ITv/5MmTs21cvXp1yc8//vGPp2XLlmUB4Iknnpj+z//5PyU/j9uO87z85S9P06ZNS3/5l3+Z3ve+96XnPOc52XljW2fOnJl+53d+J+3YsaPi0sd6bU88xvGYHXfccenv//7vB35+5513ZufZunXrwGn3339/dtpjjz1Wdtt++ctfple84hXZ7yt+b6effnr6zne+U/I7W7VqVfqjP/qjgYrF4vtU6/bX+rv82Mc+NvCYxTa+6lWvKvn54cOH05/+6Z9mAW08h+P3U+zDH/5wOvnkk7Pf31FHHZX+5//8n2nnzp0DPy/cj9iOZz7zmdlz/vHHH0/79u1Lf/Inf5IWL16cXfbMM8/MHl8AoL0I0QCAllmzZk06//zzszDlP/7jP7Jg5G//9m/TX/zFX5Sc7+/+7u+ysOTf//3f0yc+8YksuHnxi1+cnvvc56Yf/vCH6Zvf/GZav359es1rXjPkbV1yySXpySefzMKJL3/5y+lTn/pU2rBhw6Dz/fmf/3l2Pf/5n/+ZbdvrXve6tHnz5mHvx+7du7PQ63Of+1y2jbF9EXYV3Hbbbenyyy9Pf/zHf5yFf29+85vTZZddlu64446S64lQJkKfn/zkJ+kNb3jDQPAUgdjXv/717BCh4wc/+MGmbM973/vedNFFF2W/m3gc4jp+/vOfp5GKQCke09tvvz39+Mc/zioUf+u3fisLkgpLcJcsWZLe//73Z6FnHMqpdvtr+V3G8+gd73hHdtsPPvhg9pz6b//tvw16HkbI9YMf/CBde+212Xm//e1vD/w8KspuuOGG9MADD2Tn/Zd/+ZcsdMv/bj70oQ9lAV+c78gjj8xC4rvuuivdfPPN2ba++tWvzh6bhx9+eMSPNQDQAP0AAA106aWX9o8bN65/2rRpA4dXvepV2c/e/e5395944on9hw8fHjj/jTfe2D99+vT+Q4cOZcdf+MIX9j/3uc8tuc4PfOAD/eeee27JaatXr+6PjzYPPvjgwOUuv/zy7P9//vOfZz+79957B87/8MMPZ6f9zd/8zcBpcfw973nPwPGdO3dmp/2///f/hrx/N910U3aeu+++e+C0wu394Ac/yI6fddZZ/W9605tKLvfqV7+6//zzzy+57SuuuKLkPCtWrOifOnVq//bt2wdOe+c739l/5plnNmV7/vAP/7DkPHG7b3nLW7L/v+OOO7LzbNmyZeDnP/7xj7PTHn300YFtmTlzZv9wTjrppP6PfOQjA8ePOeaYkt9Jueupdvtr+V1++ctf7u/r6yt5rIvF8+k3fuM3Sk47/fTT+6+88soh79utt97aP2fOnJL7Edtw//33D5y2atWq7O9jzZo1JZc9++yz+6+66qohrxsAaD6VaABAw/3mb/5mttSvcIhqnRBVTc973vMGlu2F5z//+VnF0hNPPDFw2qmnnlpyfVEZFVVHsSSwcHj6058+ULmVF5VF48ePT7/2a782cNrxxx+fZs+ePei8z372swf+P6qO+vr6BirWTjrppIHbi2WOBXHdUU1XENsSy/YKVVvxb9yvYnE8X9V12mmnDdqeWMY5Y8aMgeOx5LVcBV2xem1P/G7yx0dbiRbLFp/xjGdk2xOPY1xfoRKtWtVu/3C/y7yXvOQl6ZhjjsmWrcbS4M9//vNZ1dhQ11fudxFLU88+++xsWWb8zuJ6Nm3aVHI9UVFZfD1RdRi90U444YSS53NUHJZ7LgMArWOwAADQcBFgRGg1msvnw5hYBhjL4vIi2BiNfDP/CPiiF1b4xje+kTX8D41odJ+/n5W2p5UKzfB/VfT1K4XHZigRoMXyx+uuuy57PsRjGH3HYlhEI9Ty2EXodd9992XLfb/1rW+lq6++Oltee++99w70Yxvu+qIP3H//7/89veUtb8mW0kbftH/7t39Lb3zjG7P7F33ZQtzn4tA4nsvRH/BHP/rRQJ/AggjTAID2oRINAGiZqEiKXlDFQUz08IpAI3pjDSUqyqKfVFRpRRhTfCgXREXj+RhWEH24Ch555JG0ZcuWmrY3KpUKtxPVRgVx3dFTq7jyLfqQxf0r3M+4X8XieDSXb4R6bc/dd9896HjhOgrTVYv7lkWV4XDiNl7/+tdnfd+iAX80588PIYhKrajMGk6jHs+o4DvnnHOyfmfRmyy2LfqaVSNCsAjU/vqv/zr9+q//elZZFj34Kom+fnF/o6It/1yOxwcAaB8q0QCAlonphTEd8+1vf3vWXD3Cnsk3B9cAAAKvSURBVBUrVqTly5cPVDqV89a3vjV9+tOfTq997WsHpiVGKBaN2aNhe76iJ5YzRjjyB3/wB9nwgqgoiqb0+aqgkYrri/sQy1QjiIn7EkFKTB0N73znO7MG9xGYxHZ87Wtfy5roF0+mrKd6bc+tt96aLTH9jd/4jWx54z333JMNfggR8sQEyqjWisqrhx56KAuQhhOTL+N2ooowHvcYXJCvDItg9F//9V+zIQYxvTImsuY14vGMoQ0rV67MhgnEMt+oOoxtiwC2GvF4RCXeRz7ykez+FYZgVBJhWww8iMEX8fjFfXrqqaey4Qux7DMmygIA7UElGgDQMlHNFWFFhDOnnHJK+sM//MNs+dt73vOeYS+3aNGiLKSICp5zzz03q2q64oorsmV3Q4VvMaly/vz5WUgSlVBvetObsoq3yZMnj/p+xFK9K6+8Mv3u7/5u1psrluHdcsstAz9/5Stfmf7X//pf2TLG6Kv2yU9+Mt10003pRS960ahvu5HbE9MtI5iMMCcevy9+8YsD1V4R1MXxX/ziF9nPY2ltfqpq3oc//OEsoDrrrLOyoOm8884r6VMXYuJlVIAtW7ZsoNotrxGPZzx3IoiLqa9R6RYBWNy/uP5qxPM37l88Ds961rOy0PGaa66p6rKx7RGiRbAboV3cv1hGevTRR4/4/gAA9Tcmpgs04HoBANpaDC6ISqpCM/hu8dnPfjYLFGP55mhEpdhtt92WBToAAFjOCQD0iOhtFU3co2ot+njFMtBYOhiVaQAAUIkQDQDoCdGv6t3vfnfW9yqWccaSwlhyl5+4CAAA5VjOCQAAAAAVGCwAAAAAABUI0QAAAACgAiEaAAAAAFQgRAMAAACACoRoAAAAAFCBEA0AAAAAKhCiAQAAAEAFQjQAAAAAqECIBgAAAABpeP8f/tGmVwhUlDwAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 1500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Make Figure 7.1\n",
    "democexec=vparty2.loc[((pd.notna(vparty2.intlmigpc))&\n",
    "                          (pd.notna(vparty2.immig_none))&\n",
    "                          (vparty2.prev_exec_pos=='Senior')&\n",
    "                          (vparty2.democracy==1)&\n",
    "                          (vparty2.WBgroup!='L')&\n",
    "                          (vparty2.year>=2000))].copy()\n",
    "fig, ax = plt.subplots(figsize=(15,10))\n",
    "custom_cycler = (cycler(color=['k', 'dimgray', 'lightgray', 'k']) +\n",
    "                 cycler(lw=[1, 2, 3, 1]) +\n",
    "                cycler(linestyle=['-','--','-','--'])\n",
    "                )\n",
    "ax.set_prop_cycle(custom_cycler)\n",
    "\n",
    "for y in ['immig_none','immig_pro','immig_anti']:\n",
    "    democexec.sort_values(by=['intlmigpc'],inplace=True)\n",
    "    y_average = democexec[y].rolling(window=100).mean()\n",
    "    x_average = democexec['intlmigpc'].rolling(window=100).mean()\n",
    "    plt.plot(x_average,y_average,label=y)\n",
    "ax.legend(loc='best')\n",
    "plt.xlabel('Foreign-born population share')\n",
    "plt.ylabel('Share of incumbent party positions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c707434e-5e82-442b-8baa-8e10c05cd6fc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
